Integrated analysis identifies a novel lncRNA prognostic signature associated with aerobic glycolysis and hub pathways in breast cancer

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.

Revisiting the hallmarks of cancer: A new look at long noncoding RNAs in breast cancer

Breast cancer is one of the leading causes of death in women worldwide. The increasing understanding of the molecular mechanisms underlying its heterogeneity favors a better understanding of tumor biology and consequently the development of better diagnostic and treatment techniques. The advent of tumor genome sequencing techniques has highlighted more participants in the process, in addition to protein-coding genes. Thus, it is now known that long noncoding RNAs, previously described as transcriptional noise with no biological function, are intimately associated with tumor development. In breast cancer, they are abnormally expressed and closely associated with tumor progression, which makes them attractive diagnostic biomarkers and prognostic and specific therapeutic targets. Therefore, a thorough understanding of the regulatory mechanisms of long noncoding RNAs in breast cancer is essential for the search for new treatment strategies. In this review, we summarize the major long noncoding RNAs and their association with the cancer characteristics of the ability to sustain proliferative signaling, evasion of growth suppressors, replicative immortality, activation of invasion and metastasis, induction of angiogenesis, resistance to cell death, reprogramming of energy metabolism, genomic instability and sustained mutations, promotion of tumor inflammation, and evasion of the immune system. In addition, we report and suggest how they can be used as prognostic biomarkers and possible therapeutic targets.

Systematic identification and characterization of lncRNAs and lncRNA-miRNA-mRNA networks in the liver of turbot (Scophthalmus maximus L.) induced with Vibrio anguillarum

Long noncoding RNAs (lncRNAs), can regulate mRNA by targeting miRNA in a competing endogenous RNA network, have become a hot topic in the research of fish immune mechanism recent years. While in turbot (Scophthalmus maximus L.), an economically important marine fish, there are limited researches about the role of lncRNAs in its immune response to bacterial infection. In this study, a total of 184 differentially expressed lncRNAs (DElncRNAs) were systematically identified and characterized using whole-transcriptome sequencing of the liver of turbot challenged with Vibrioanguillarum at 0 h (control) and three different time points post infection (2 h, 12 h and 24 h, respectively). Subsequently, GO and KEGG signaling pathways of differentially expressed lncRNAs were analyzed to predict their function. We found that lncRNAs in our results were significantly enriched in several immune-related signaling pathways, including the NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, Cytokine-cytokine receptor, MAPK signaling pathway, phagosome, PPAR signaling pathway and the regulation of autophagy. In addition, a total of 492 DE lncRNA - DE miRNA -DE mRNA networks were identified at three different time points post infection, which were consisted of 102 networks at 2 h, 122 networks at 12 h and 81 networks at 24 h post infection, respectively. Noticeably, 92 of these regulated networks were immune-related. These observations suggested that lncRNAs can regulate the expression of immune-related genes in the response to bacterial infection in turbot. Moreover, our findings would provide a new insight into the immune response of turbot to pathogen infection and lay a foundation for future study.

A prognostic Risk Score model for oral squamous cell carcinoma constructed by 6 glycolysis-immune-related genes

Background

Oral squamous cell carcinoma (OSCC) is the most frequent tumor of the head and neck. The glycolysis-related genes and immune-related genes have been proven prognostic values in various cancers. Our study aimed to test the prognostic value of glycolysis-immune-related genes in OSCC.

Methods

Data of OSCC patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Enrichment analysis was applied to the glycolysis- and immune-related genes screened by differential expression analysis. Univariate Cox and LASSO Cox analyses were used to filtrate the genes related to the prognosis of OSCC and to construct Risk Score model.

Results

A Risk Score model was constructed by six glycolysis-immune-related genes (including ALDOC, VEGFA, HRG, PADI3, IGSF11 and MIPOL1). High risk OSCC patients (Risk Score >−0.3075) had significantly worse overall survival than that of low risk patients (Risk Score <−0.3075).

Conclusions

The Risk Score model constructed basing on 6 glycolysis-immune-related genes was reliable in stratifying OSCC patients with different prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02358-0.

Integrated Analysis of RNA Binding Protein-Related lncRNA Prognostic Signature for Breast Cancer Patients

Long non-coding RNAs (lncRNAs) have been well known for their multiple functions in the tumorigenesis, development, and prognosis of breast cancer (BC). Mechanistically, their production, function, or stability can be regulated by RNA binding proteins (RBPs), which were also involved in the carcinogenesis and progression of BC. However, the roles and clinical implications of RBP-related lncRNAs in BC remain largely unknown. Therefore, we herein aim to construct a prognostic signature with RBP-relevant lncRNAs for the prognostic evaluation of BC patients. Firstly, based on the RNA sequencing data of female BC patients from The Cancer Genome Atlas (TCGA) database, we screened out 377 differentially expressed lncRNAs related to RBPs. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were then performed to establish a prognostic signature composed of 12-RBP-related lncRNAs. Furthermore, we divided the BC patients into high- and low-risk groups by the prognostic signature and found the overall survival (OS) of patients in the high-risk group was significantly shorter than that of the low-risk group. Moreover, the 12-lncRNA signature exhibited independence in evaluating the prognosis of BC patients. Additionally, a functional enrichment analysis revealed that the prognostic signature was associated with some cancer-relevant pathways, including cell cycle and immunity. In summary, our 12-lncRNA signature may provide a theoretical reference for the prognostic evaluation or clinical treatment of BC patients.

Integrated analysis identifies a novel lncRNA prognostic signature associated with aerobic glycolysis and hub pathways in breast cancer

Long noncoding RNAs (lncRNAs) play a crucial role in cancer aerobic glycolysis. However, glycolysis‐related lncRNAs are still underexplored in breast cancer. In this study, we identified the five most glycolysis‐related lncRNAs in breast cancer to construct a prognostic signature, which could distinguish between patients with unfavorable and favorable prognoses. To investigate the role of signature lncRNAs in breast cancer, we profiled their expression levels in breast cancer progression cell line model. Real‐time PCR revealed that the five lncRNAs could contribute to breast cancer initiation or progression. Furthermore, we observed that the levels of four lncRNAs expression had a significant trend of gradient upregulation with the addition of glycolysis inhibitor in breast cancer cells. Afterward, random forest and logistic regression were conducted to assess the model's performance in stratifying glycolysis status. Finally, a nomogram including the lncRNA signature and clinical features was developed, and its efficacy in predicting the survival time and clinical utility was evaluated using a calibration curve, concordance index, and decision curve analysis. In this study, gene set enrichment analysis showed that the mTOR pathway, a central pathway in tumor initiation and progression, was significantly enriched in the high‐risk group. In addition, gene set variation analysis was performed to validate our findings in two independent datasets. Subsequent weighted gene co‐expression network analysis, followed by enrichment analysis, indicated that downstream cell growth‐related signaling was strikingly activated in the high‐risk group, and may directly promote tumor progression and escalate mortality risk in patients with high‐risk scores. Overall, our findings may provide novel insight into lncRNA‐related metabolic regulation, and help to develop promising prognostic indicators and therapeutic targets for breast cancer patients.