A Novel Prognostic Signature of mRNA-lncRNA in Breast Cancer

An overview about biomarkers in breast cancer: Insights into the diagnostic and prognostic significance

Breast cancer (BC) is one of the most significant neoplasms globally due to its high incidence and mortality, particularly among females. As a highly heterogeneous pathology, biomarkers are essential for characterizing specific tumors. Currently, several biological processes are well-described in the context of this neoplasm, such as alterations in BRCA1/2, HER, and pathways involving estrogen and progesterone hormone receptors. These studies have enabled the use of these findings as more precise methods for diagnosis, prognosis, and treatment. However, beyond patients who do not exhibit these classic markers, some individuals within the same risk group respond differently to treatment. Therefore, the search for biological markers that can improve diagnosis, aid in stratification, or serve as therapeutic targets is continuous and urgent. Genetic signatures have led to molecular tests currently used in clinical practice, though certain limitations persist. Understanding genetic and epigenetic mechanisms facilitates the identification of potential biomarkers. Biomarker targets must undergo experimental and clinical trials on samples of significant size before reaching clinical utility. In this review, we compile the classical markers and describe the potential use of other markers associated with the biological processes of this neoplasm.

A Comprehensive Bioinformatic Analysis Identifies a Tumor Suppressor Landscape of the MEG3 lncRNA in Breast Cancer

Breast cancer (BC) is the leading cause of cancer mortality in women and a major risk to world health. Therefore, effective strategies are required for prompt diagnosis and treatment. Nowadays, non-coding RNAs (ncRNAs), particularly long ncRNAs (lncRNAs), have assumed a significant role in the prognosis and diagnosis of diseases, including cancer. In the present study, surveying the bioinformatic tools, including the lncRNADisease v2.0, OncoDB, InteractiVenn, GEPIA, RAID, COXPRESdb, DAVID v6.8, GEO2R, and LncSEA, we proposed the Maternally Expressed Gene (MEG3) as a potential biomarker in BC. This lncRNA significantly downregulates in BC and is associated with tumor size, metastasis, and pathological stage. MEG3 expression is downregulated in several types of primary human cancers and tumor cell lines, which raises the possibility that it could act as a tumor suppressor. The results suggest that MEG3 may play a crucial role in fundamental pathways, including apoptosis, and interact with essential genes and proteins such as P53. It may also be associated with the prognosis, proliferation, migration, invasion, and metastasis of BC.

The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.

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.

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.

Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients

Background

Prostate cancer (PCa) is the most common malignancy and the leading cause of cancer death in men. Recent studies suggest the molecular signature was more effective than the clinical indicators for the prognostic prediction, but all of the known studies focused on a single RNA type. The present study was to develop a new prognostic signature by integrating long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) and evaluate its prognostic performance.

Methods

The RNA expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) or Gene Expression Omnibus database (GSE17951, GSE7076, and GSE16560). The PCa-driven modules were identified by constructing a weighted gene coexpression network, the corresponding genes of which were overlapped with differentially expressed RNAs (DERs) screened by the MetaDE package. The optimal prognostic signature was screened using the least absolute shrinkage and selection operator analysis. The prognostic performance and functions of the combined prognostic signature was then assessed.

Results

Twelve PCa-driven modules were identified using TCGA dataset and validated in the GSE17951 and GSE7076 datasets, and six of them were considered to be preserved. A total of 217 genes in these 6 modules were overlapped with 699 DERs, from which a nine-gene prognostic signature was identified (including 3 lncRNAs and 6 mRNAs), and the risk score of each patient was calculated. The overall survival was significantly shortened in patients having the risk score higher than the cut-off, which was demonstrated in TCGA (p = 5.063E − 03) dataset and validated in the GSE16560 (p = 3.268E − 02) dataset. The prediction accuracy of this risk score was higher than that of clinical indicators (the Gleason score and prostate-specific antigen) or the single RNA type, with the area under the receiver operator characteristic curve of 0.945. Besides, some new therapeutic targets and mechanisms (MAGI2-AS3-SPARC/GJA1/CYSLTR1, DLG5-AS1-DEFB1, and RHPN1-AS1-CDC45/ORC) were also revealed.

Conclusion

The risk score system established in this study may provide a novel reliable method to identify PCa patients at a high risk of death.