A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

A CRISPR-dCas13 RNA-editing tool to study alternative splicing

Alternative splicing allows multiple transcripts to be generated from the same gene to diversify the protein repertoire and gain new functions despite a limited coding genome. It can impact a wide spectrum of biological processes, including disease. However, its significance has long been underestimated due to limitations in dissecting the precise role of each splicing isoform in a physiological context. Furthermore, identifying key regulatory elements to correct deleterious splicing isoforms has proven equally challenging, increasing the difficulty of tackling the role of alternative splicing in cell biology. In this work, we take advantage of dCasRx, a catalytically inactive RNA targeting CRISPR-dCas13 ortholog, to efficiently switch alternative splicing patterns of endogenous transcripts without affecting overall gene expression levels cost-effectively. Additionally, we demonstrate a new application for the dCasRx splice-editing system to identify key regulatory RNA elements of specific splicing events. With this approach, we are expanding the RNA toolkit to better understand the regulatory mechanisms underlying alternative splicing and its physiological impact in various biological processes, including pathological conditions.

Overcoming cancer drug-resistance calls for novel strategies targeting abnormal alternative splicing

Abnormal gene alternative splicing (AS) events are strongly associated with cancer progression. Here, we summarize AS events that contribute to the development of drug resistance and classify them into three categories: alternative cis-splicing (ACS), alternative trans-splicing (ATS), and alternative back-splicing (ABS). The regulatory mechanisms underlying AS processes through cis-acting regulatory elements and trans-acting factors are comprehensively described, and the distinct functions of spliced variants, including linear spliced variants derived from ACS, chimeric spliced variants arising from ATS, and circRNAs generated through ABS, are discussed. The identification of dysregulated spliced variants, which contribute to drug resistance and hinder effective cancer treatment, suggests that abnormal AS processes may together serve as a precise regulatory mechanism enabling drug-resistant cancer cell survival or, alternatively, represent an evolutionary pathway for cancer cells to adapt to changes in the external environment. Moreover, this review summarizes recent advancements in treatment approaches targeting AS-associated drug resistance, focusing on cis-acting regulatory elements, trans-acting factors, and specific spliced variants. Collectively, gaining an in-depth understanding of the mechanisms underlying aberrant alternative splicing events and developing strategies to target this process hold great promise for overcoming cancer drug resistance.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

A hybrid machine learning model for timely prediction of breast cancer

Breast cancer is one of the leading causes of untimely deaths among women in various countries across the world. This can be attributed to many factors including late detection which often increase its severity. Thus, detecting the disease early would help mitigate its mortality rate and other risks associated with it. This study developed a hybrid machine learning model for timely prediction of breast cancer to help combat the disease. The dataset from Kaggle was adopted to predict the breast tumor growth and sizes using random tree classification, logistic regression, XBoost tree and multilayer perceptron on the dataset. The implementation of these machine learning algorithms and visualization of the results was done using Python. The results achieved a high accuracy (99.65%) on training and testing datasets which is far better than traditional means. The predictive model has good potential to enhance early detection and diagnosis of breast cancer and improvement of treatment outcome. It could also assist patients to timely deal with their condition or life patterns to support their recovery or survival.

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.

Microbiota-host crosstalk in the newborn and adult rumen at single-cell resolution

BACKGROUND

The rumen is the hallmark organ of ruminants, playing a vital role in their nutrition and providing products for humans. In newborn suckling ruminants milk bypasses the rumen, while in adults this first chamber of the forestomach has developed to become the principal site of microbial fermentation of plant fibers. With the advent of single-cell transcriptomics, it is now possible to study the underlying cell composition of rumen tissues and investigate how this relates the development of mutualistic symbiosis between the rumen and its epithelium-attached microbes.

RESULTS

We constructed a comprehensive cell landscape of the rumen epithelium, based on single-cell RNA sequencing of 49,689 high-quality single cells from newborn and adult rumen tissues. Our single-cell analysis identified six immune cell subtypes and seventeen non-immune cell subtypes of the rumen. On performing cross-species analysis of orthologous genes expressed in epithelial cells of cattle rumen and the human stomach and skin, we observed that the species difference overrides any cross-species cell-type similarity. Comparing adult with newborn cattle samples, we found fewer epithelial cell subtypes and more abundant immune cells, dominated by T helper type 17 cells in the rumen tissue of adult cattle. In newborns, there were more fibroblasts and myofibroblasts, an IGFBP3+ epithelial cell subtype not seen in adults, while dendritic cells were the most prevalent immune cell subtype. Metabolism-related functions and the oxidation-reduction process were significantly upregulated in adult rumen epithelial cells. Using 16S rDNA sequencing, fluorescence in situ hybridization, and absolute quantitative real-time PCR, we found that epithelial Desulfovibrio was significantly enriched in the adult cattle. Integrating the microbiome and metabolome analysis of rumen tissues revealed a high co-occurrence probability of Desulfovibrio with pyridoxal in the adult cattle compared with newborn ones while the scRNA-seq data indicated a stronger ability of pyroxidal binding in the adult rumen epithelial cell subtypes. These findings indicate that Desulfovibrio and pyridoxal likely play important roles in maintaining redox balance in the adult rumen.

CONCLUSIONS

Our integrated multi-omics analysis provides novel insights into rumen development and function and may facilitate the future precision improvement of rumen function and milk/meat production in cattle.

A Regulatory Axis between Epithelial Splicing Regulatory Proteins and Estrogen Receptor α Modulates the Alternative Transcriptome of Luminal Breast Cancer

Epithelial splicing regulatory proteins 1 and 2 (ESRP1/2) control the splicing pattern during epithelial to mesenchymal transition (EMT) in a physiological context and in cancer, including breast cancer (BC). Here, we report that ESRP1, but not ESRP2, is overexpressed in luminal BCs of patients with poor prognosis and correlates with estrogen receptor α (ERα) levels. Analysis of ERα genome-binding profiles in cell lines and primary breast tumors showed its binding in the proximity of ESRP1 and ESRP2 genes, whose expression is strongly decreased by ERα silencing in hormone-deprived conditions. The combined knock-down of ESRP1/2 in MCF-7 cells followed by RNA-Seq, revealed the dysregulation of 754 genes, with a widespread alteration of alternative splicing events (ASEs) of genes involved in cell signaling, metabolism, cell growth, and EMT. Functional network analysis of ASEs correlated with ESRP1/2 expression in ERα+ BCs showed RAC1 as the hub node in the protein-protein interactions altered by ESRP1/2 silencing. The comparison of ERα- and ESRP-modulated ASEs revealed 63 commonly regulated events, including 27 detected in primary BCs and endocrine-resistant cell lines. Our data support a functional implication of the ERα-ESRP1/2 axis in the onset and progression of BC by controlling the splicing patterns of related genes.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-β, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.

Histone marks regulate the epithelial-to-mesenchymal transition via alternative splicing

Histone modifications impact final splicing decisions. However, there is little evidence of the driving role of these marks in inducing cell-specific splicing changes. Using CRISPR epigenome editing tools, we show in an epithelial-to-mesenchymal cell reprogramming system (epithelial-to-mesenchymal transition [EMT]) that a single change in H3K27ac or H3K27me3 levels right at the alternatively spliced exon is necessary and sufficient to induce a splicing change capable of recapitulating important aspects of EMT, such as cell motility and invasiveness. This histone-mark-dependent splicing effect is highly dynamic and mediated by direct recruitment of the splicing regulator PTB to its RNA binding sites. These results support a role for H3K27 marks in inducing a change in the cell's phenotype via regulation of alternative splicing. We propose the dynamic nature of chromatin as a rapid and reversible mechanism to coordinate the splicing response to cell-extrinsic cues, such as induction of EMT.

The oncogenic kinase NEK2 regulates an RBFOX2-dependent pro-mesenchymal splicing program in triple-negative breast cancer cells

BACKGROUND

Triple-negative breast cancer (TNBC) is the most heterogeneous and malignant subtype of breast cancer (BC). TNBC is defined by the absence of expression of estrogen, progesterone and HER2 receptors and lacks efficacious targeted therapies. NEK2 is an oncogenic kinase that is significantly upregulated in TNBC, thereby representing a promising therapeutic target. NEK2 localizes in the nucleus and promotes oncogenic splice variants in different cancer cells. Notably, alternative splicing (AS) dysregulation has recently emerged as a featuring trait of TNBC that contributes to its aggressive phenotype.

METHODS

To investigate whether NEK2 modulates TNBC transcriptome we performed RNA-sequencing analyses in a representative TNBC cell line (MDA-MB-231) and results were validated in multiple TNBC cell lines. Bioinformatics and functional analyses were carried out to elucidate the mechanism of splicing regulation by NEK2. Data from The Cancer Genome Atlas were mined to evaluate the potential of NEK2-sensitive exons as markers to identify the TNBC subtype and to assess their prognostic value.

RESULTS

Transcriptome analysis revealed a widespread impact of NEK2 on the transcriptome of TNBC cells, with 1830 AS events that are susceptible to its expression. NEK2 regulates the inclusion of cassette exons in splice variants that discriminate TNBC from other BC and that correlate with poor prognosis, suggesting that this kinase contributes to the TNBC-specific splicing program. NEK2 elicits its effects by modulating the expression of the splicing factor RBFOX2, a well-known regulator of epithelial to mesenchymal transition (EMT). Accordingly, NEK2 splicing-regulated genes are enriched in functional terms related to cell adhesion and contractile cytoskeleton and NEK2 depletion in mesenchymal TNBC cells induces phenotypic and molecular traits typical of epithelial cells. Remarkably, depletion of select NEK2-sensitive splice-variants that are prognostic in TNBC patients is sufficient to interfere with TNBC cell morphology and motility, suggesting that NEK2 orchestrates a pro-mesenchymal splicing program that modulates migratory and invasive properties of TNBC cells.

CONCLUSIONS

Our study uncovers an extensive splicing program modulated by NEK2 involving splice variants that confer an invasive phenotype to TNBCs and that might represent, together with NEK2 itself, valuable therapeutic targets for this disease.

circ_0075943 Dominates the miR-141-3p/AK2 Network to Support the Development of Breast Carcinoma

Background

Breast cancer (BC) progression is related to the disorder of circular RNAs (circRNAs). This study aims to characterize the role of circ_0075943 in BC.

Methods

Real-time fluorescent quantitative PCR (real-time PCR) technology was implemented to investigate circ_0075943, AK2 mRNA, and microRNA-141-3p levels. MTT, colony formation method, Transwell, and flow cytometry technique were adopted to investigate cell function. The connection between miR-141-3p and circ_0075943 or AK2 was confirmed by the dual-luciferase reporter gene or RNA immunoprecipitation (RIP). The influence on circ_0075943 in vivo was confirmed by animal experiments.

Results

circ_0075943 was augmented in BC cell lines and tumor specimens. Dwindling of circ_0075943 could dramatically suppress the phenotype of BC cells and induce apoptosis. MiR-141-3p is a target of circ_0075943, and its repression largely reverses the influence of knocking down circ_0075943 on cell behavior. Moreover, AK2, as a target of miR-141-3p, is augmented in BC cells and specimens. AK2 overexpression could restore the phenotype of BC cells blocked by miR-141-3p redevelopment. Moreover, knocking down circ_0075943 could suppress the growth of tumors in vivo.

Conclusion

The abnormal regulation of circ_0075943 participates in part of the expansion of BC by dominating the miR-141-3p/AK2 regulatory network.