Individualized lncRNA differential expression profile reveals heterogeneity of breast cancer

Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

Metabolism-regulating non-coding RNAs in breast cancer: roles, mechanisms and clinical applications

Breast cancer is one of the most common malignancies that pose a serious threat to women's health. Reprogramming of energy metabolism is a major feature of the malignant transformation of breast cancer. Compared to normal cells, tumor cells reprogram metabolic processes more efficiently, converting nutrient supplies into glucose, amino acid and lipid required for malignant proliferation and progression. Non-coding RNAs(ncRNAs) are a class of functional RNA molecules that are not translated into proteins but regulate the expression of target genes. NcRNAs have been demonstrated to be involved in various aspects of energy metabolism, including glycolysis, glutaminolysis, and fatty acid synthesis. This review focuses on the metabolic regulatory mechanisms and clinical applications of metabolism-regulating ncRNAs involved in breast cancer. We summarize the vital roles played by metabolism-regulating ncRNAs for endocrine therapy, targeted therapy, chemotherapy, immunotherapy, and radiotherapy resistance in breast cancer, as well as their potential as therapeutic targets and biomarkers. Difficulties and perspectives of current targeted metabolism and non-coding RNA therapeutic strategies are discussed.

Personalized differential expression analysis in triple-negative breast cancer

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Epigenetic modifications: Key players in cancer heterogeneity and drug resistance

Cancer heterogeneity and drug resistance remain pivotal obstacles in effective cancer treatment and management. One major contributor to these challenges is epigenetic modifications - gene regulation that does not involve changes to the DNA sequence itself but significantly impacts gene expression. As we elucidate these phenomena, we underscore the pivotal role of epigenetic modifications in regulating gene expression, contributing to cellular diversity, and driving adaptive changes that can instigate therapeutic resistance. This review dissects essential epigenetic modifications - DNA methylation, histone modifications, and chromatin remodeling - illustrating their significant yet complex contributions to cancer biology. While these changes offer potential avenues for therapeutic intervention due to their reversible nature, the interplay of epigenetic and genetic changes in cancer cells presents unique challenges that must be addressed to harness their full potential. By critically analyzing the current research landscape, we identify knowledge gaps and propose future research directions, exploring the potential of epigenetic therapies and discussing the obstacles in translating these concepts into effective treatments. This comprehensive review aims to stimulate further research and aid in developing innovative, patient-centered cancer therapies. Understanding the role of epigenetic modifications in cancer heterogeneity and drug resistance is critical for scientific advancement and paves the way towards improving patient outcomes in the fight against this formidable disease.

Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA

Annotation of genome data with biological features is a challenging problem. One such problem deals with distinguishing lncRNA from mRNA. In this study, three groups of classification features, namely base periodicity, physicochemical property and nucleotide compositions were considered. We are attempting to propose a simple neural network model to obtain better results using judicious combination of the above said sequence features. Our approach uses balanced dataset, simple prediction model and use of limited features in distinguishing lncRNA and mRNA. Accordingly (a) two properties of base periodicity: peak power spectrum of the signal and noise-to-signal ratio (SNR) of this peak signal (b) three physicochemical properties: solvation, stacking and hydrogen-bonding energy and (c) all dinucleotides and trinucleotides compositions were used. Classification was performed by considering features independently followed by combining these properties for improvement. Classification metric was used to compare the result for seven eukaryotic organisms for various combinations of features. Nucleotide compositions combined with physicochemical property or base periodicity group of features becomes a strong classifier with more than 99 percentage accuracy. Base periodicity analysis with SNR can be used as discriminating feature of lncRNA from mRNA.

Upregulation of lncRNA PTOV1-AS1 in hepatocellular carcinoma contributes to disease progression and sorafenib resistance through regulating miR-505

Increasing evidence has displayed the vital influence of lncRNA in tumorigenesis and chemoresistance of cancer treatment. This study investigated the function of lncRNA PTOV1-AS1 in hepatocellular carcinoma (HCC) and its role in sorafenib resistance. The relative expression of lncRNA and miRNA was measured by RT-qPCR. The cellular activities including cell proliferation and invasion were explored by CCK-8 and Transwell assays. Bioinformatics analysis and dual-luciferase reporter assay were used to predict the targeting miRNA of PTOV1-AS1. The expression levels of PTOV1-AS1 were higher in HCC tissues than that in the normal tissues and associated with patients' overall survival. Knockdown of PTOV1-AS1 decreased cell proliferation rate and invasion number. After treatment with different concentrations of sorafenib, the sorafenib-resistant hepatoma cells were conducted. PTOV1-AS1 expression levels were increased in HepG2-SR and Huh7-SR cells. PTOV1-AS1 knockdown repressed the proliferation, invasion, and drug resistance of sorafenib-resistant HCC cells by targeting the expression of miR-505. In conclusion, the expression of PTOV1-AS1 is increased in HCC and sorafenib-resistance HCC cells, as well as is associated with patients' prognosis. Inhibition of PTOV1-AS1 expression can reduce the resistance of sorafenib-resistant HCC cells, which may play a role by targeting the negative regulation of miR-505 expression.

Multi-omics integration analysis unveils heterogeneity in breast cancer at the individual level

Identifying robust breast cancer subtypes will help to reveal the cancer heterogeneity. However, previous breast cancer subtypes were based on population-level quantitative gene expression, which is affected by batch effects and cannot be applied to individuals. We detected differential gene expression, genomic, and epigenomic alterations to identify driver differential expression at the individual level. The individual driver differential expression reflected the breast cancer patients' heterogeneity and revealed four subtypes. Mesenchymal subtype as the most aggressive subtype harbored deletion and downregulated expression of genes in chromosome 11q23 region. Specifically, silencing of the SDHD gene in 11q23 promoted the invasion and migration of breast cancer cells in vitro by the epithelial-mesenchymal transition. The immunologically hot subtype displayed an immune-hot microenvironment, including high T-cell infiltration and upregulated PD-1 and CTLA4. Luminal and genomic-unstable subtypes showed opposite macrophage polarization, which may be regulated by the ligand-receptor pairs of CD99. The integration of multi-omics data at the individual level provides a powerful framework for elucidating the heterogeneity of breast cancer.

Interactome of Long Non-Coding RNAs: Transcriptomic Expression Patterns and Shaping Cancer Cell Phenotypes

RNA biology has gained extensive recognition in the last two decades due to the identification of novel transcriptomic elements and molecular functions. Cancer arises, in part, due to the accumulation of mutations that greatly contribute to genomic instability. However, the identification of differential gene expression patterns of wild-type loci has exceeded the boundaries of mutational study and has significantly contributed to the identification of molecular mechanisms that drive carcinogenic transformation. Non-coding RNA molecules have provided a novel avenue of exploration, providing additional routes for evaluating genomic and epigenomic regulation. Of particular focus, long non-coding RNA molecule expression has been demonstrated to govern and direct cellular activity, thus evidencing a correlation between aberrant long non-coding RNA expression and the pathological transformation of cells. lncRNA classification, structure, function, and therapeutic utilization have expanded cancer studies and molecular targeting, and understanding the lncRNA interactome aids in defining the unique transcriptomic signatures of cancer cell phenotypes.

Identification of associations between lncRNA and drug resistance based on deep learning and attention mechanism

Introduction

Abnormal lncRNA expression can lead to the resistance of tumor cells to anticancer drugs, which is a crucial factor leading to high cancer mortality. Studying the relationship between lncRNA and drug resistance becomes necessary. Recently, deep learning has achieved promising results in predicting biomolecular associations. However, to our knowledge, deep learning-based lncRNA-drug resistance associations prediction has yet to be studied.

Methods

Here, we proposed a new computational model, DeepLDA, which used deep neural networks and graph attention mechanisms to learn lncRNA and drug embeddings for predicting potential relationships between lncRNAs and drug resistance. DeepLDA first constructed similarity networks for lncRNAs and drugs using known association information. Subsequently, deep graph neural networks were utilized to automatically extract features from multiple attributes of lncRNAs and drugs. These features were fed into graph attention networks to learn lncRNA and drug embeddings. Finally, the embeddings were used to predict potential associations between lncRNAs and drug resistance.

Results

Experimental results on the given datasets show that DeepLDA outperforms other machine learning-related prediction methods, and the deep neural network and attention mechanism can improve model performance.

Dicsussion

In summary, this study proposes a powerful deep-learning model that can effectively predict lncRNA-drug resistance associations and facilitate the development of lncRNA-targeted drugs. DeepLDA is available at https://github.com/meihonggao/DeepLDA.

Integrative network analysis reveals subtype-specific long non-coding RNA regulatory mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC.

Identification of immune-related signature for the prognosis and benefit of immunotherapy in triple-negative breast cancer

Background: There is a lack of biomarkers for predicting the efficacy of immunotherapy in triple-negative breast cancer (TNBC). Hence, we constructed an immune risk score (IRS) model to predict the prognosis of patients with TNBC and evaluate those who are sensitive to immunotherapy. Methods: The ribonucleic acid (RNA) sequencing data, mutation data, and clinical information of TNBC patients were obtained from The Cancer Genome Atlas database. Data of immune-related genes were obtained from the Import and InnateDB databases. The IRS model was constructed using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, and the predictive ability of the prognostic model was evaluated. Further external validation was performed using the Gene Expression Omnibus (GEO) databases GSE58812 and GSE135565. Data on the clinical characteristics, immune landscape, and immune checkpoint inhibitors used in different risk groups were analyzed. Finally, the drug sensitivity of the patients in the high- and low-risk groups was predicted. Results: The prognostic risk score model comprised six genes: HSPA6, LCN1, ARTN, IL36G, BCL2A1, and CASP12. The area under the curve values at 1 year, 3 years, and 5 years were 0.835, 0.852, and 0.843, respectively, indicating that the model has a good potential for predicting the long-term survival of TNBC patients, which is consistent with the results of the GEO cohort. Compared with the high-risk group, the low-risk group had a better prognosis; more abundant immune-activated cell infiltrates, such as CD8+ T cells and CD4 memory-activated T cells, and a higher enrichment of immune-related signaling pathways, such as the cytokine receptor interaction, nucleotide oligomerization domain-like receptor signal pathway, T-cell receptor signal pathway, and B-cell receptor signaling pathway, were observed. In addition, the immune checkpoint encoding genes, such as CD274, CTLA4, PDCD1, and PDCD1LG2 were highly expressed in the low-risk group, which showed that this group was more likely to benefit from immunotherapy. Conclusion: A new IRS gene feature was established to predict the patients' prognosis and guide immunotherapy. Moreover, it was revealed that several potential therapeutic drugs can be used in high-risk patients who are unresponsive to immunotherapy.

The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM

INTRODUCTION

Skin cutaneous melanoma (SKCM) is the world's fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.

MATERIALS AND METHODS

The TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound-healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.

RESULTS

A 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.

CONCLUSION

PRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.

Nanobiotherapeutic strategies to target immune microenvironment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the subtype with the least favourable outcomes in breast cancer. Besides chemotherapy, there is a chronic lack of other effective treatments. Advances in omic technologies have liberated us from the ambiguity of TNBC heterogeneity in terms of cancer cell and immune microenvironment in recent years. This new understanding of TNBC pathology has already led to the exploitation of novel nanoparticulate systems, including tumor vaccines, oncolytic viruses, and antibody derivatives. The revolutionary ideas in the therapeutic landscape provide new opportunities for TNBC patients. Translating these experimental medicines into clinical benefit is both appreciated and challenging. In this review, we describe the prospective nanobiotherapy of TNBC that has been developed to overcome clinical obstacles, and provide our vision for this booming field at the overlap of cancer biotherapy and nanomaterial design.

Tris(2-Pyridylmethylamine)V(O)2 Complexes as Counter Ions of Diprotonated Decavanadate Anion: Potential Antineoplastic Activity

The synthesis and theoretical-experimental characterization of a novel diprotanated decavanadate is presented here due to our search for novel anticancer metallodrugs. Tris(2-pyridylmethyl)amine (TPMA), which is also known to have anticancer activity in osteosarcoma cell lines, was introduced as a possible cationic species that could act as a counterpart for the decavanadate anion. However, the isolated compound contains the previously reported vanadium (V) dioxido-tpma moieties, and the decavanadate anion appears to be diprotonated. The structural characterization of the compound was performed by infrared spectroscopy and single-crystal X-ray diffraction. In addition, DFT calculations were used to analyze the reactive sites involved in the donor-acceptor interactions from the molecular electrostatic potential maps. The level of theory mPW1PW91/6–31G(d)-LANL2DZ and ECP = LANL2DZ for the V atom was used. These insights about the compounds’ main interactions were supported by analyzing the noncovalent interactions utilizing the AIM and Hirshfeld surfaces approach. Molecular docking studies with small RNA fragments were used to assess the hypothesis that decavanadate’s anticancer activity could be attributed to its interaction with lncRNA molecules. Thus, a combination of three potentially beneficial components could be evaluated in various cancer cell lines.

Guanxin V protects against ventricular remodeling after acute myocardial infarction through the interaction of TGF-β1 and Vimentin

BACKGROUND

Our previous study demonstrated that Guanxin V (GXV), a traditional Chinese herbal medicine, has a significant therapeutic effect on ventricular remodeling. However, the mechanistic action of GXV in ventricular remodeling warrants clarification.

PURPOSE

Here, we aimed to explore the anti-ventricular remodeling contribution of GXV and to provide an experimental basis for clinical generalization.

METHODS

A ventricular remodeling model after acute myocardial infarction was constructed in Syrian hamsters. The echocardiography and biochemical indices of cardiac function and remodeling were evaluated in different groups. Moreover, we built a remodeling model in cardiomyocytes and further explored the mechanism. Transmission electron microscopy was used to observe the ultrastructure of cardiomyocytes. The vital markers involved in the signaling pathway were detected by RT-qPCR and immunoblotting. Transforming growth factor beta 1 (TGF-β1) was overexpressed with lentivirus to verify the necessity of TGF-β1 in GXV's anti-ventricular remodeling effect. Finally, co-immunoprecipitation was conducted to test the interaction of TGF-β1 and Vimentin.

RESULTS

In hamster cardiac remodeling induced by acute myocardial infarction, GXV alleviated apoptosis, cardiac hypertrophy, and cardiac remodeling, and even improved cardiac function. Mechanistically, GXV inhibited the remodeling process by directly targeting TGF-β1. Overexpression of TGF-β1 exacerbated the ventricular remodeling, whereas GXV reversed this dysregulation. GXV also decreased the up-regulated Vimentin level in pathological ventricular remodeling. Moreover, the interaction of Vimentin and TGF-β1 was confirmed by co-immunoprecipitation, and GXV impeded this interaction.

CONCLUSION

We showed that the interaction of Vimentin and TGF-β1 may be a novel target for ventricular remodeling and that GXV might be a new agent to fight against ventricular remodeling by targeting TGF-β1 and impeding its interaction with Vimentin.

Single-Cell RNA-Seq Reveals Heterogeneous lncRNA Expression in Xenografted Triple-Negative Breast Cancer Cells

Breast cancer is the most commonly diagnosed cancer in the world, with triple-negative breast cancer (TNBC) making up 12% of these diagnoses. TNBC tumours are highly heterogeneous in both inter-tumour and intra-tumour gene expression profiles, where they form subclonal populations of varying levels of aggressiveness. These aspects make it difficult to study and treat TNBC, requiring further research into tumour heterogeneity as well as potential therapeutic targets and biomarkers. Recently, it was discovered that the majority of the transcribed genome comprises non-coding RNAs, in particular long non-coding RNAs (lncRNAs). LncRNAs are transcripts of >200 nucleotides in length that do not encode a protein. They have been characterised as regulatory molecules and their expression can be associated with a malignant phenotype. We set out to explore TNBC tumour heterogeneity in vivo at a single cell level to investigate whether lncRNA expression varies across different cells within the tumour, even if cells are coming from the same cell line, and whether lncRNA expression is sufficient to define cellular subpopulations. We applied single-cell expression profiling due to its ability to capture expression signals of lncRNAs expressed in small subpopulations of cells. Overall, we observed most lncRNAs to be expressed at low, but detectable levels in TNBC xenografts, with a median of 25 lncRNAs detected per cell. LncRNA expression alone was insufficient to define a subpopulation of cells, and lncRNAs showed highly heterogeneous expression patterns, including ubiquitous expression, subpopulation-specific expression, and a hybrid pattern of lncRNAs expressed in several, but not all subpopulations. These findings reinforce that transcriptionally defined tumour cell subpopulations can be identified in cell-line derived xenografts, and uses single-cell RNA-seq (scRNA-seq) to detect and characterise lncRNA expression across these subpopulations in xenografted tumours. Future studies will aim to investigate the spatial distribution of lncRNAs within xenografts and patient tissues, and study the potential of subclone-specific lncRNAs as new therapeutic targets and/or biomarkers.

2-Aminopyrimidinium Decavanadate: Experimental and Theoretical Characterization, Molecular Docking, and Potential Antineoplastic Activity

The interest in decavanadate anions has increased in recent decades, since these clusters show interesting applications as varied as sensors, batteries, catalysts, or new drugs in medicine. Due to the capacity of the interaction of decavanadate with a variety of biological molecules because of its high negative charge and oxygen-rich surface, this cluster is being widely studied both in vitro and in vivo as a treatment for several global health problems such as diabetes mellitus, cancer, and Alzheimer’s disease. Here, we report a new decavanadate compound with organic molecules synthesized in an aqueous solution and structurally characterized by elemental analysis, infrared spectroscopy, thermogravimetric analysis, and single-crystal X-ray diffraction. The decavanadate anion was combined with 2-aminopyrimidine to form the compound [2-ampymH]6[V10O28]·5H2O (1). In the crystal lattice, organic molecules are stacked by π–π interactions, with a centroid-to-centroid distance similar to that shown in DNA or RNA molecules. Furthermore, computational DFT calculations of Compound 1 corroborate the hydrogen bond interaction between pyrimidine molecules and decavanadate anions, as well as the π–π stacking interactions between the central pyrimidine molecules. Finally, docking studies with test RNA molecules indicate that they could serve as other potential targets for the anticancer activity of decavanadate anion.