LncRNA polymorphisms and breast cancer risk

Exosomal noncoding RNA (ncRNA) in breast cancer pathogenesis and therapy; two sides of the same coin

Over the past few years, breast cancer has become the most prevalent type of cancer globally, with the primary cause of death from the disease being metastatic cancer. This has led to the development of early detection techniques, mainly using non-invasive biomarkers in a range of body fluids. Exosomes are unique extracellular vesicles (EVs) transmitting cellular signals over great distances via various cargo. They are readily apparent in physiological fluids due to release by breast cancer cells or breast cancer-tumor microenvironment (TME) cells. In light of this, numerous biological and functional facets of human tumours, such as breast cancer, are intimately associated with exosomal noncoding RNAs (ncRNAs), containing miRNAs (microRNAs), lncRNAs (long noncoding RNAs), and circRNAs (circular RNAs). Exosomal ncRNAs serve a critical role in various steps of breast cancer development, enabling the exchange of genetic information between cancer cells and other cells (e.g., immune cells), thus regulating tumour angiogenesis, growth, metastasis, immune responses and drug resistance. They interact with multiple regulatory complexes with dissimilar enzymatic actions, which, in turn, modify the chromatin sceneries, including nucleosome modifications, DNA methylation, and histone modifications. Herein, we look into the exosomes' underlying regulatory mechanisms in breast cancer. Furthermore, we inspect the existing understanding of the functions of exosomal miRNAs, lncRNAs, and circRNAs in breast cancer to authenticate their possible significance in identifying biomarkers, deciphering their role in immune escape and drug resistance, and finally, analyzing treatment practices.

Oncogenic mechanisms of COL10A1 in cancer and clinical challenges (Review)

Collagen type X α1 chain (COL10A1), a gene encoding the α‑1 chain of type X collagen, serves a key role in conferring tensile strength and structural integrity to tissues. Upregulation of COL10A1 expression has been observed in different malignancies, including lung, gastric and pancreatic cancer, and is associated with poor prognosis. The present review provides an updated synthesis of the evolving biological understanding of COL10A1, with a particular focus on its mechanisms of action and regulatory functions within the context of tumorigenesis. For example, it has been established that increased COL10A1 expression promotes cancer progression by activating multiple signaling pathways, including the TGF‑β1/Smad, MEK/ERK and focal adhesion kinase signaling pathways, thereby inducing proliferation, invasion and migration. Additionally, COL10A1 has been demonstrated to induce epithelial‑mesenchymal transition and reshapes the extracellular matrix within tumor tissues. Furthermore, on the basis of methyltransferase‑like 3‑mediated N6‑methyladenosine methylation, COL10A1 intricately regulates the epitranscriptomic machinery, thereby augmenting its oncogenic role. However, although COL10A1 serves a pivotal role in gene transcription and the orchestration of tumor growth, the question of whether COL10A1 would serve as a viable therapeutic target remains a subject of scientific hypothesis requiring rigorous examination. Variables such as distinct tumor microenvironments and treatment associations necessitate further experimental validation. Therefore, a comprehensive assessment and understanding of the functional and mechanistic roles of COL10A1 in cancer may pave the way for the development of innovative cancer treatment strategies.

Roles of the lncRNAs MEG3, PVT1 and H19 tagSNPs in gastric cancer susceptibility

BACKGROUND

Improper expression of long noncoding RNAs (lncRNAs) can cause various cancers. Single nucleotide polymorphisms (SNPs) affect the expression and function of several key lncRNAs. We assessed the associations of MEG3, PVT1, and H19 lncRNA polymorphisms with susceptibility to gastric cancer (GC).

METHODS

In Ardabil (a high-risk area in North‒West Iran), 795 blood samples were collected from 396 cases and 399 controls. The control subjects were randomly selected from individuals receiving regular physical examinations in this hospital with no self-reported cancer history and were frequency-matched to the case group by sex and 5-year age intervals. All the samples were genotyped via the Infinium HTS platform, which was subsequently followed by rigorous data quality control, as well as statistical and bioinformatic analyses.

RESULTS

The H19 rs2107425 SNP was associated with GC risk in a recessive model of inheritance (TT vs. CC + CT: OR = 1.87). The PVT1 rs13255292 variant in the overdominant model significantly reduced GC risk (CT vs. CC + TT: OR = 0.74). There was no significant association between H19 rs2839698, MEG3 rs116907618, or rs11160608, or PVT1 rs7017386, rs13254990 tagSNPs and susceptibility to GC. The interaction between H19 rs2107425 TT and PVT1 rs7017386 TC increased GC risk (OR = 3.73; pbon < 0.05). The MEG3, PVT1, and H19 variants were not associated with clinicopathologic characteristics.

CONCLUSIONS

We revealed significant associations of the H19 rs2107425 and PVT1 rs13255292 genetic variants with GC. Interestingly, the novel SNP‒SNP interaction of H19 and PVT1 tagSNPs had a greater effect than single SNP impacts did on GC risk, providing us with invaluable data to identify potential biological mechanisms involved in the development of GC.

As a novel prognostic model for breast cancer, the identification and validation of telomere-related long noncoding RNA signatures

BACKGROUND

Telomeres are a critical component of chromosome integrity and are essential to the development of cancer and cellular senescence. The regulation of breast cancer by telomere-associated lncRNAs is not fully known, though. The goals of this study were to describe predictive telomere-related LncRNAs (TRL) in breast cancer and look into any possible biological roles for these RNAs.

METHODS

We obtained RNA-seq data, pertinent clinical data, and a list of telomere-associated genes from the cancer genome atlas and telomere gene database, respectively. We subjected differentially expressed TRLs to co-expression analysis and univariate Cox analysis to identify a prognostic TRL. Using LASSO regression analysis, we built a prognostic model with 14 TRLs. The accuracy of the model's prognostic predictions was evaluated through the utilization of Kaplan-Meier (K-M) analysis as well as receiver operating characteristic (ROC) curve analysis. Additionally, immunological infiltration and immune drug prediction were done using this model. Patients with breast cancer were divided into two subgroups using cluster analysis, with the latter analyzed further for variations in response to immunotherapy, immune infiltration, and overall survival, and finally, the expression of 14-LncRNAs was validated by RT-PCR.

RESULTS

We developed a risk model for the 14-TRL, and we used ROC curves to demonstrate how accurate the model is. The model may be a standalone prognostic predictor for patients with breast cancer, according to COX regression analysis. The immune infiltration and immunotherapy results indicated that the high-risk group had a low level of PD-1 sensitivity and a high number of macrophages infiltrating. In addition, we've discovered a number of small-molecule medicines with considerable for use in treating high-risk groups. The cluster 2 subtype showed the highest immune infiltration, the highest immune checkpoint expression, and the worst prognosis among the two subtypes defined by cluster analysis, which requires more attention and treatment.

CONCLUSION

As a possible biomarker, the proposed 14-TRL signature could be utilized to evaluate clinical outcomes and treatment efficacy in breast cancer patients.

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime

Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.

Bridging autoimmunity and epigenetics: The influence of lncRNA MALAT1

Autoimmune disorders represent a heterogeneous spectrum of conditions defined by an immune system's atypical reactivity against endogenous constituents. In the complex anatomy of autoimmune pathogenesis, lncRNAs have appeared as pivotal arbiters orchestrating the mechanisms of ailment initiation, immune cascades, and transcriptional modulation. One such lncRNA, MALAT1, has garnered attention for its potential association with the aetiology of several autoimmune diseases. MALAT1 has been shown to influence a wide spectrum of cellular processes, which include cell multiplication and specialization, as well as apoptosis and inflammation. In autoimmune diseases, MALAT1 exhibits both disease-specific and shared patterns of dysregulation, often correlating with disease severity. The molecular mechanisms underlying MALAT1's impact on autoimmune disorders include epigenetic modifications, alternative splicing, and modulation of gene expression networks. Additionally, MALAT1's intricate interactions with microRNAs, other lncRNAs, and protein-coding genes further underscore its role in immune regulation and autoimmune disease progression. Understanding the contribution of MALAT1 in autoimmune pathogenesis across different diseases could offer valuable insights into shared pathways, thereby clearing a path for the creation of innovative and enhanced therapeutic approaches to address these complex disorders. This review aims to elucidate the complex role of MALAT1 in autoimmune disorders, encompassing rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease (Crohn's disease and ulcerative colitis), type 1 diabetes, systemic lupus erythematosus, and psoriasis. Furthermore, it discusses the potential of MALAT1 as a diagnostic biomarker, therapeutic target, and prognostic indicator.

Long noncoding RNA polymorphisms in gynecological cancers

Gynecological malignancies are one of the main causes of cancer-induced mortality. Despite remarkable recent therapeutic advances, current therapeutic options are not sufficient. Regarding the effect of long noncoding RNAs (lncRNAs) on cell differentiation, proliferation and apoptosis, variations in their expression cause different anomalies, such as tumorigenesis. SNPs influence lncRNA function and expression. LncRNA polymorphisms can predict cancer risk and are effective for early diagnosis and customized therapy. In this literature review, we comprehensively investigate the effect of lncRNA polymorphisms on gynecological cancers. LncRNA-related variants are proposed to evaluate cancer incidence, early detection and management of personalized therapy. Nonetheless, more studies are required to validate the consistency of current findings in numerous samples and across various ethnic groups.

LncRNA polymorphisms and lung cancer risk

Lung cancer (LC) imposes a significant burden, and is associated with high mortality and morbidity among malignant tumors. Aberrant expression of particular lncRNAs is closely linked to LC. LncRNA polymorphisms cause abnormal expression levels and/or structural dysfunction. They can affect the progression of cancer, survival, response to chemotherapy and recurrence rates in cancer patients. The present article provides a comprehensive overview of the effect of lncRNA genetic polymorphisms on LC. It is proposed that lncRNA-related variants can be used to predict cancer risk and therapeutic outcomes. More large-scale trials on diverse ethnic groups are required to validate the results, thus personalizing LC therapy based on lncRNA genotypes.

The expression landscape and pangenome of long non-coding RNA in the fungal wheat pathogen Zymoseptoria tritici

Long non-coding RNAs (lncRNAs) are regulatory molecules interacting in a wide array of biological processes. lncRNAs in fungal pathogens can be responsive to stress and play roles in regulating growth and nutrient acquisition. Recent evidence suggests that lncRNAs may also play roles in virulence, such as regulating pathogenicity-associated enzymes and on-host reproductive cycles. Despite the importance of lncRNAs, only a few model fungi have well-documented inventories of lncRNA. In this study, we apply a recent computational pipeline to predict high-confidence lncRNA candidates in Zymoseptoria tritici, an important global pathogen of wheat impacting global food production. We analyse genomic features of lncRNAs and the most likely associated processes through analyses of expression over a host infection cycle. We find that lncRNAs are frequently expressed during early infection, before the switch to necrotrophic growth. They are mostly located in facultative heterochromatic regions, which are known to contain many genes associated with pathogenicity. Furthermore, we find that lncRNAs are frequently co-expressed with genes that may be involved in responding to host defence signals, such as oxidative stress. Finally, we assess pangenome features of lncRNAs using four additional reference-quality genomes. We find evidence that the repertoire of expressed lncRNAs varies substantially between individuals, even though lncRNA loci tend to be shared at the genomic level. Overall, this study provides a repertoire and putative functions of lncRNAs in Z. tritici enabling future molecular genetics and functional analyses in an important pathogen.

The Intergenic Type LncRNA (LINC RNA) Faces in Cancer with In Silico Scope and a Directed Lens to LINC00511: A Step toward ncRNA Precision

BACKGROUND

Long intergenic non-coding RNA, is one type of lncRNA, exerting various cellular activities, as does ncRNA, including the regulation of gene expression and chromatin remodeling. The abnormal expression of lincRNAs can induce or suppress carcinogenesis.

MAIN BODY

LincRNAs can regulate cancer progression through different mechanisms and are considered as potential drug targets. Genetic variations such as single nucleotide polymorphisms (SNPs) in lincRNAs may affect gene expression and messenger ribonucleic acid (mRNA) stability. SNPs in lincRNAs have been found to be associated with different types of cancer, as well. Specifically, LINC00511 has been known to promote the progression of multiple malignancies such as breast cancer, colorectal cancer, lung cancer, hepatocellular carcinoma, and others, making it a promising cancer prognostic molecular marker.

CONCLUSION

LincRNAs have been proved to be associated with different cancer types through various pathways. Herein, we performed a comprehensive literature and in silico databases search listing lncRNAs, lincRNAs including LINC00511, lncRNAs' SNPs, as well as LINC00511 SNPs in different cancer types, focusing on their role in various cancer types and mechanism(s) of action.

Prognostic and immune microenvironment analysis of cuproptosis-related LncRNAs in breast cancer

Breast cancer is the most common tumor and the leading cause of cancer death in women. Cuproptosis is a new type of cell death, which can induce proteotoxic stress and eventually lead to cell death. Therefore, regulating copper metabolism in tumor cells is a new therapeutic approach. Long non-coding RNAs play an important regulatory role in immune response. At present, cuproptosis-related lncRNAs in breast cancer have not been reported. Breast cancer RNA sequencing, genomic mutations, and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Patients with breast cancer were randomly assigned to the train group or the test group. Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-related lncRNAs and to construct a risk prognostic model. The prediction performance of the model is verified and recognized. In addition, the nomogram was used to predict the prognosis of breast cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassay were used to detect the differences in biological function. Tumor mutation burden (TMB) was used to measure immunotherapy response. A total of 19 cuproptosis genes were obtained and a prognostic model based on 10 cuproptosis-related lncRNAs was constructed. Kaplan-Meier survival curves showed statistically significant overall survival (OS) between the high-risk and low-risk groups. Receiver operating characteristic curve (ROC) and principal component analysis (PCA) show that the model has accurate prediction ability. Compared with other clinical features, cuproptosis-related lncRNAs model has higher diagnostic efficiency. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor for breast cancer patients. In addition, the nomogram model analysis showed that the tumor mutation burden was significantly different between the high-risk and low-risk groups. Of note, the additive effect of patients in the high-risk group and patients with high TMB resulted in reduced survival in breast cancer patients. Our study identified 10 cuproptosis-related lncRNAs, which may be promising biomarkers for predicting the survival prognosis of breast cancer.

Overexpression of Estrogen Receptor α in Mammary Glands of Aging Mice Is Associated with a Proliferative Risk Signature and Generation of Estrogen Receptor α-Positive Mammary Adenocarcinomas

Age is a risk factor for human estrogen receptor-positive breast cancer, with highest prevalence following menopause. While transcriptome risk profiling is available for human breast cancers, it is not yet developed for prognostication for primary or secondary breast cancer development utilizing at-risk breast tissue. Both estrogen receptor α (ER) and aromatase overexpression have been linked to human breast cancer. Herein, conditional genetically engineered mouse models of estrogen receptor 1 (Esr1) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) were used to show that induction of Esr1 overexpression just before or with reproductive senescence and maintained through age 30 months resulted in significantly higher prevalence of estrogen receptor-positive adenocarcinomas than CYP19A1 overexpression. All adenocarcinomas tested showed high percentages of ER+ cells. Mammary cancer development was preceded by a persistent proliferative transcriptome risk signature initiated within 1 week of transgene induction that showed parallels to the Prosigna/Prediction Analysis of Microarray 50 human prognostic signature for early-stage human ER+ breast cancer. CYP19A1 mice also developed ER+ mammary cancers, but histology was more divided between adenocarcinoma and adenosquamous, with one ER- adenocarcinoma. Results demonstrate that, like humans, generation of ER+ adenocarcinoma in mice was facilitated by aging mice past the age of reproductive senescence. Esr1 overexpression was associated with a proliferative estrogen pathway-linked signature that preceded appearance of ER+ mammary adenocarcinomas.

Classic and New Markers in Diagnostics and Classification of Breast Cancer

Simple Summary

With ever-increasing incidence, breast cancer is considered a most diagnosed type of cancer among women worldwide. Breast cancer arises through malignant transformation of ductal or lobular cells in female (or male) breast and the genetic, phenotypic and morphological heterogeneity has an effect on tumour’s behaviour, thereby instigating a need for individual personalized therapy. A traditional assessment of tumour’s characteristics involves a biopsy and histological analysis of a tumour tissue, and in recent years has been accompanied by analysis of molecular biomarkers to enhance the results. In this work we aimed to thoroughly investigate the latest data in this field of study and give a comprehensive review of novel molecular biomarkers of breast cancer and methodologies used to analyse them.

Abstract

Breast cancer remains the most frequently diagnosed form of female’s cancer, and in recent years it has become the most common cause of cancer death in women worldwide. Like many other tumours, breast cancer is a histologically and biologically heterogeneous disease. In recent years, considerable progress has been made in diagnosis, subtyping, and complex treatment of breast cancer with the aim of providing best suited tumour-specific personalized therapy. Traditional methods for breast cancer diagnosis include mammography, MRI, biopsy and histological analysis of tumour tissue in order to determine classical markers such as estrogen and progesterone receptors (ER, PR), cytokeratins (CK5/6, CK14, C19), proliferation index (Ki67) and human epidermal growth factor type 2 receptor (HER2). In recent years, these methods have been supplemented by modern molecular methodologies such as next-generation sequencing, microRNA, in situ hybridization, and RT-qPCR to identify novel molecular biomarkers. MicroRNAs (miR-10b, miR-125b, miR145, miR-21, miR-155, mir-30, let-7, miR-25-3p), altered DNA methylation and mutations of specific genes (p16, BRCA1, RASSF1A, APC, GSTP1), circular RNA (hsa_circ_0072309, hsa_circRNA_0001785), circulating DNA and tumour cells, altered levels of specific proteins (apolipoprotein C-I), lipids, gene polymorphisms or nanoparticle enhanced imaging, all these are promising diagnostic and prognostic tools to disclose any specific features from the multifaceted nature of breast cancer to prepare best suited individualized therapy.

Prognostic Analysis of LncRNA MCM3AP-AS1 in Colorectal Cancer and the Mechanism of Its Effect on Tumor Cell Activity

To determine the clinical prognostic significance of lncRNA MCM3AP-AS1 in colorectal cancer (CRC) and its preliminary mechanism, 43 CRC patients and 48 healthy individuals were analyzed. Peripheral blood MCM3AP-AS1 was quantified via qRT–PCR in CRC patients at admission and 2 h after surgery and in healthy individuals. Human colon cancer cells (HCT116 and SW480) were transfected with shRNAs targeting upregulation of MCM3AP-AS1 expression (named as sh-MCM3AP-AS1 group) and corresponding negative RNAs (named as sh-MCM3AP-AS1 group). Additionally, the cells were then treated either with 50 mM of the VEGF-specific inhibitor PTK787 (Selleck, USA) (named as inhibition group) or normal saline as a control (named as control group). Before therapy, CRC patients presented a higher MCM3AP-AS1 level than healthy individuals (P < 0.05), and the sensitivity and specificity of MCM3AP-AS1 in predicting the occurrence of CRC were 65.12% and 83.33%, respectively (P < 0.001). After therapy, CRC patients presented a decrease in MCM3AP-AS1 levels, and recurrence was higher in patients who died (P < 0.05). Additionally, the high MCM3AP-AS1 expression group presented a higher mortality than the low MCM3AP-AS1 expression group (P < 0.05). In an in vitro assay, CRC cells showed a higher MCM3AP-AS1 level than CCD-18Co cells, and the sh-MCM3AP-AS1 group presented decreased cell proliferation and invasiveness, whereas the levels apoptosis-associated proteins were increased (P < 0.05). Moreover, the VEGF and VEGFR2 mRNA levels were increased in CRC cells, and VEGF/VEGFR2 pathway-associated proteins were inhibited in the sh-MCM3AP-AS1 group (P < 0.05). Moreover, treatment with PTK787 decreased cell proliferation and invasivness but increased the levels of apoptosis-associated proteins (P < 0.05).

Long noncoding RNA polymorphisms and colorectal cancer risk: Progression and future perspectives

Colorectal cancer (CRC) is one of the most common cancers causing death worldwide. Many long noncoding RNAs (lncRNAs) have possible carcinogenic or tumor suppressor functions. Some lncRNA polymorphisms are useful for predicting cancer risk, and may help advance personalized therapy management. While the use of lncRNAs as biomarkers is promising, there are still drawbacks, and further studies are needed to verify the consistency of current outcomes in large-scale populations and different ethnicities. Single nucleotide polymorphisms (SNPs) can disrupt a lncRNAs' function, thus enhancing or hindering disease occurrence. SNPs can directly influence the lncRNA expression by interfering with transcription factor binding or affecting indirectly a regulatory factors' expression. Moreover, the association between lncRNAs and other RNAs or proteins may be disrupted by SNPs. This research sought to assess the association between lncRNA polymorphisms and CRC risk, as well as clinical and therapeutic consequences in certain cases.