Long noncoding RNAs (lncRNAs) are emerging as critical regulators of cancer biology, yet their roles in breast cancer, particularly in triple-negative breast cancer (TNBC), remain incompletely understood. Through a custom siRNA library screen targeting TNBC-associated lncRNAs in MDA-MB-231 and BT-549 TNBC cell models, we identified LURAP1L-AS1 as a key modulator of TNBC progression. Survival analysis of TNBC patients demonstrated a significant association between elevated LURAP1L-AS1 expression and poor clinical outcomes. LURAP1L-AS1 knockdown significantly impaired colony formation and organoid growth of TNBC models, associated with increased apoptosis thus highlighting its role in promoting tumorigenicity. RNA sequencing of LURAP1L-AS1-depleted cells revealed dysregulation of pathways related to cell proliferation, apoptosis, migration, and RNA processing. Bioinformatics analysis predicted LURAP1L-AS1 to function as a competitive endogenous RNA (ceRNA), sponging key microRNAs, such as miR-7a-5p, miR-101-3p, miR-181a-5p, and miR-27a-3p, thereby modulating oncogenes including EZH2, MCL1, and KRAS, which are linked to increased cancer cell survival, proliferation, and metastasis. In addition to its role in TNBC, correlation analysis using breast cancer patient datasets revealed a significant association between LURAP1L-AS1 and ESR1 expression, suggesting its broader impact across breast cancer subtypes. Concordantly, LURAP1L-AS1 depletion inhibited estrogen receptor-positive (ER+) MCF7 breast cancer cells colony formation and organotypic growth. Our findings establish LURAP1L-AS1 as a functional lncRNA that promotes breast cancer progression, highlighting its potential for use in RNA-based therapies for breast cancer.

The persistently high mortality rates associated with cancer underscore the imperative need for innovative, efficacious, and safer therapeutic agents, as well as a more nuanced understanding of tumor biology. Patient-derived organoids (PDOs) have emerged as innovative preclinical models with significant translational potential, capable of accurately recapitulating the structural, functional, and heterogeneous characteristics of primary tumors. When integrated with cutting-edge genomic tools such as CRISPR, PDOs provide a powerful platform for identifying cancer driver genes and novel therapeutic targets. This comprehensive review delves into recent advancements in CRISPR-mediated functional screens leveraging PDOs across diverse cancer types, highlighting their pivotal role in high-throughput functional genomics and tumor microenvironment (TME) modeling. Furthermore, this review highlights the synergistic potential of integrating PDOs with CRISPR screens in cancer immunotherapy, focusing on uncovering immune evasion mechanisms and improving the efficacy of immunotherapeutic approaches. Together, these cutting-edge technologies offer significant promise for advancing precision oncology.

Human essential proteins (HEPs) are indispensable for individual viability and development. However, experimental methods to identify HEPs are often costly, time consuming and labor intensive. In addition, existing computational methods predict HEPs only at the cell line level, but HEPs vary across living human, cell line and animal models. Here we develop a sequence-based deep learning model, Protein Importance Calculator (PIC), by fine-tuning a pretrained protein language model. PIC not only substantially outperforms existing methods for predicting HEPs but also provides comprehensive prediction results across three levels: human, cell line and mouse. Furthermore, we define the protein essential score, derived from PIC, to quantify human protein essentiality and validate its effectiveness by a series of biological analyses. We also demonstrate the biomedical value of the protein essential score by identifying potential prognostic biomarkers for breast cancer and quantifying the essentiality of 617,462 human microproteins.

Essential genes are indispensable for the survival of cancer cell. CRISPR/Cas9-based pooled genetic screens have distinguished the essential genes and their functions in distinct cellular processes. Nevertheless, the landscape of essential genes at the single cell levels and the effect on the tumor microenvironment (TME) remains limited. Here, we identified 396 essential protein-coding genes (ESPs) by integration of 8 genome-wide CRISPR loss-of-function screen datasets of colorectal cancer (CRC) cell lines and single-cell RNA sequencing (scRNA-seq) data of CRC tissues. Then, 29 essential long non-coding genes (ESLs) were predicted using Hypergeometric Test (HT) and Personalized PageRank (PPR) algorithms based on ESPs and co-expressed network constructed from scRNA-seq. CRISPR/Cas9 knockout experiment verified the effect of several ESPs and ESLs on the survival of CRC cell line. Furthermore, multi-omics features of ESPs and ESLs were illustrated by examining their expression patterns and transcription factor (TF) regulatory network at the single cell level, as well as DNA mutation and DNA methylation events at bulk level. Finally, through integrating multiple intracellular regulatory networks with cell-cell communication network (CCN), we elucidated that CD47 and MIF are regulated by multiple CRC essential genes, and the anti-cancer drugs sunitinib can interfere the expression of them potentially. Our findings provide a comprehensive asset of CRC ESPs and ESLs, sheding light on the mining of potential therapy targets for CRC.

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Breast cancer, a highly heterogeneous and complex disease, remains the leading cause of cancer-related death among women worldwide. Despite advances in treatment modalities, effective prognostic models and therapeutic strategies are still urgently needed.

We retrospectively analyzed 15 independent breast cancer cohorts to explore the role of RNA modifications in the prognosis of patients with breast cancer. By integrating nine types of RNA modifications, we developed a comprehensive machine learning-based RNA modification signature (CMRS). Furthermore, single-cell RNA sequencing data were analyzed to understand the biological mechanisms underlying CMRS. In addition, immune infiltration levels were evaluated via six different algorithms, and immune checkpoint inhibitor responsiveness was predicted. Moreover, the response of high-CMIS patients to chemotherapy was predicted via multiple datasets. Finally, immunohistochemistry was performed on tissue samples from breast cancer patients to validate protein expression levels.

Our analysis revealed five key RNA modification-related genes (ENO1, ARAF, WT1, GADD45A, and BIRC3) associated with breast cancer prognosis. The CMRS model demonstrated high predictive accuracy across multiple cohorts and was significantly correlated with patient survival outcomes. Multiomics analysis revealed that high CMRS was associated with increased tumor mutational burden and distinct mutational signatures, particularly in pathways related to TP53, MYC, and cell proliferation. Single-cell analysis highlighted the involvement of epithelial cells and MYC signaling in high CMRS activity. Cell‒cell communication analysis revealed reduced interaction strength in hig CMRS patients, indicating poor prognosis. Furthermore, low CMRS patients presented increased immune cell infiltration and improved responsiveness to immune checkpoint inhibitors, whereas high CMRS patients were identified as potential candidates for treatment with panobinostat and vincristine.

Our study elucidates the significant role of RNA modifications in breast cancer prognosis and treatment. The CMRS model serves as a sensitive biomarker for predicting patient survival and treatment responsiveness, offering a new avenue for personalized therapy in patients with breast cancer.

Genetic studies have associated thousands of enhancers with breast cancer (BC). However, the vast majority have not been functionally characterized. Thus, it remains unclear how BC-associated enhancers contribute to cancer.

Here, we perform single-cell CRISPRi screens of 3513 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of > 500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of BC-associated enhancers disrupts breast cancer gene programs. We observe BC-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple BC-associated enhancers indirectly regulate TP53. Comparative studies illustrate subtype specific functions between enhancers in ER + and ER - cells. Finally, we develop the pySpade package to facilitate analysis of single-cell enhancer screens.

Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Chemoresistance in triple-negative breast cancer (TNBC) presents a significant clinical hurdle, limiting the efficacy of treatments like doxorubicin. This study aimed to explore the molecular changes associated with doxorubicin resistance and identify potential therapeutic targets to overcome this resistance, thereby improving treatment outcomes for TNBC patients.

Doxorubicin-resistant (DoxR) TNBC models (MDA-MB-231 and BT-549) were generated by exposing cells to increasing concentrations of doxorubicin. RNA sequencing (RNA-Seq) was performed using the Illumina platform, followed by bioinformatics analysis with CLC Genomics Workbench and iDEP. Functional assays assessed proliferation, sphere formation, migration, and cell cycle changes. Protein expression and phosphorylation were confirmed via Western blotting. Pathway and network analyses were conducted using Ingenuity Pathway Analysis (IPA) and STRING, while survival analysis was performed using Kaplan-Meier Plotter database.

DoxR cells exhibited reduced proliferation, sphere formation, and migration, but showed enhanced tolerance to doxorubicin. Increased CHK2 and p53 phosphorylation indicated cellular dormancy as a resistance mechanism. RNA-Seq analysis revealed upregulation of cytokine signaling and stress-response pathways, while cholesterol and lipid biosynthesis were suppressed. Activation of the IL1β cytokine network was prominent in DoxR cells, and CRISPR-Cas9 screens data identified dependencies on genes involved in rRNA biogenesis and metabolism. A 27-gene signature associated with doxorubicin resistance was linked to worse clinical outcomes in a large breast cancer cohort (HR = 1.76, FDR p < 2.0 × 10-13).

This study uncovers potential therapeutic strategies for overcoming TNBC resistance, including dormancy reversal and targeting onco-ribosomal pathways and cytokine signaling networks, to improve the efficacy of doxorubicin-based treatments.

EpCAM (epithelial cell adhesion molecule) protein expression was detected in 45 to 90% of breast cancers in different studies, and high expression levels were associated with poor outcomes in several retrospective analyses. This study aims to investigate the relationship between EpCAM and clinicopathological parameters and survival in breast cancer.

This study was conducted as a Quasi-Experimental Cohort Study to explore 100 breast cancer patients. After the surgical excision of breast cancer, pathology blocks were deparaffinized and subjected to IHC (immunohistochemistry) for EpCAM examination. Using a Roche VENTANA Benchmark GX automated staining instrument and a well-established IHC staining protocol, the expression of EpCAM in breast cancer tissue was assessed. Independent sample T-test and Chi squared and Logistic Regression test with STATA version 17 software were used for data analysis.

The difference in the distribution of the negative state of biomarkers (ER = estrogen receptor, PR = Progesterone receptor) and EPCAM positive group was significant (P-value = 0.002) (P-value = 0.006). A statistically insignificant distinction was observed in the distribution of the HER2 (human epidermal growth factor receptor) and EPCAM groups (P-value = 0.198). With 30.95% of those in the EPCAM-positive cohort experienced metastasis or recurrence. ER+ and PR+ decreased the chance of EPCAM positive by 0.25 and 0.29, respectively. HER2+ and Basal like breast cancer increase the chances of EPCAM being positive by 1.9 and 2.08, respectively. Basal like breast cancer increases the odds of EpCAM positive 2.19 times. Similarly, N2 and stage 3 increase the odds of EpCAM positive by 1.95 and 0.5 times, respectively.

We found that Basal like breast cancer, HER2+, and stage 3 increase the chance of EpCAM positivity. It seems that EPCAM positive cancer has more chance for recurrence and metastasis.

ENO1, also called 2-phospho-D-glycerate hydrolase in cellular glycolysis, is an enzyme that converts 2-phosphoglycerate to phosphoenolpyruvate and plays an important role in the Warburg effect. In various tumors, ENO1 overexpression correlates with poor prognosis. ENO1 is a multifunctional oncoprotein that, when located on the cell surface, acts as a "moonlighting protein" to promote tumor invasion and metastasis. When located intracellularly, ENO1 facilitates glycolysis to dysregulate cellular energy and sustain tumor proliferation. Additionally, it promotes tumor progression by activating oncogenic signaling pathways. ENO1 is a tumor biomarker and represents a promising target for tumor therapy. This review summarizes recent advances from 2020 to 2024 in understanding the relationship between ENO1 and tumors and explores the latest targeted therapeutic strategies involving ENO1.

Metabolic reprogramming is one of the hallmarks of cancer, and in breast cancer (BC), several metabolic enzymes are overexpressed and overactivated. One of these, Enolase 1 (ENO1), catalyses glycolysis and is involved in the regulation of multiple signalling pathways.

This study aimed to evaluate in silico the prognostic and predictive effects of ENO1 expression in BC.

This is a bioinformatic in silico analysis.

Using available online platforms (Kaplan-Meier [KM] plotter, receiver operating characteristic curve [ROC] plotter, cBioPortal, Genotype-2-Outcome [G-2-O], MethSurv, and Tumour-Immune System Interaction Database [TISIDB]), we performed a bioinformatic in silico analysis to establish the prognostic and predictive effects related to ENO1 expression in BC. A network analysis was performed using the Oncomine platform, and signalling, epigenetic, and immune regulation pathways were explored.

ENO1 was overexpressed in all the analysed Oncomine, epigenetic, and immune pathways in triple-negative, but not in hormone receptor-positive BCs. In human epidermal growth factor receptor 2 (HER2)-positive BCs, ENO1 expression showed a mixed profile. Analysis on disease progression and histological types showed ENO1 overexpression in ductal in situ and invasive carcinoma, in high-grade tumours followed by advanced or metastasis and was linked to worse survival. High ENO1 expression was also associated with relapse-free, distant metastasis-free and overall survival, irrespectively of treatment and was mainly related to basal subtype.

ENO1 overexpression recruits a range of signalling pathways during disease progression conferring a worse prognosis and can be potentially used as a biomarker of disease progression and therapeutic target, particularly in triple-negative and in ductal invasive carcinoma.

Malaria is a deadly disease caused by Plasmodium parasites. While potent drugs are available in the market for malaria treatment, over the years, Plasmodium parasites have successfully developed resistance against many, if not all, front-line drugs. This poses a serious threat to global malaria eradication efforts, and the continued discovery of new drugs is necessary to tackle this debilitating disease. With recent unprecedented progress in machine learning techniques, single-cell transcriptomic in Plasmodium offers a powerful tool for identifying crucial proteins as a drug target and subsequent computational prediction of potential drugs. In this study, We have implemented a mutual-information-based feature reduction algorithm with a classification algorithm to select important proteins from transcriptomic datasets (sexual and asexual stages) for Plasmodium falciparum and then constructed the protein-protein interaction (PPI) networks of the proteins. The analysis of this PPI network revealed key proteins vital for the survival of Plasmodium falciparum. Based on the function and identification of a few strong binding sites on a couple of these key proteins, we computationally predicted a set of potential drug molecules using a deep learning-based technique. Lead drug molecules that satisfy ADMET and drug-likeliness properties are finally reported out of the generated drugs. The study offers a general computational pipeline to identify crucial proteins using scRNA-seq data sets and further development of potential new drugs.

Triple negative breast cancer (TNBC) is a malignant subtype of breast cancer characterized by the absence of ER, PR, and HER2. We aimed to explore target gene from the perspective of cancer-immunity cycle, providing insights into treatment of TNBC.

We obtained TNBC samples from METABRIC database and downloaded 4 datasets from GEO database, as well as an IMvigor210 dataset. WGCNA was applied to screen genes associated with cancer-immunity cycle in TNBC. GO, KEGG and GSEA analyses were performed to explore the target gene's potential functions and pathways. The binding motifs with transcription factors were predicted with FIMO. Immune infiltration analysis was conducted by CIBERSORT.

TUBB2A was screened out as our target gene which was negatively correlated with T cell recruitment in cancer-immunity cycle. TUBB2A expressed higher in TNBC samples than in normal samples. High expression of TUBB2A was associated with poor prognosis of TNBC. 12 transcription factors and 5 miRNAs might regulate TUBB2A's expression. The infiltration ratios of 7 types of immune cells such as CD8+ T cells, naive CD4+ T cells and activated memory CD4+ T cells were significantly lower in TUBB2A high expression group. TUBB2A was a potential drug target.

We screened a cancer-immunity cycle-related gene TUBB2A which was negatively correlated with T cell recruiting in TNBC. TUBB2A expressed higher in TNBC samples than in normal samples, associated with poor prognosis.

Phosphoglycerate kinase 1 (PGK1) has been identified as a possible biomarker for breast cancer (BC) and may play a role in the development and advancement of triple-negative BC (TNBC).

To explore the PGK1 and BC research status and PGK1 expression and mechanism differences among TNBC, non-TNBC, and normal breast tissue.

PGK1 and BC related literature was downloaded from Web of Science Core Collection Core Collection. Publication counts, key-word frequency, cooperation networks, and theme trends were analyzed. Normal breast, TNBC, and non-TNBC mRNA data were gathered, and differentially expressed genes obtained. Area under the summary receiver operating characteristic curves, sensitivity and specificity of PGK1 expression were determined. Kaplan Meier revealed PGK1's prognostic implication. PGK1 co-expressed genes were explored, and Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Disease Ontology applied. Protein-protein interaction networks were constructed. Hub genes identified.

PGK1 and BC related publications have surged since 2020, with China leading the way. The most frequent keyword was "Expression". Collaborative networks were found among co-citations, countries, institutions, and authors. PGK1 expression and BC progression were research hotspots, and PGK1 expression and BC survival were research frontiers. In 16 TNBC vs non-cancerous breast and 15 TNBC vs non-TNBC datasets, PGK1 mRNA levels were higher in 1159 TNBC than 1205 non-cancerous breast cases [standardized mean differences (SMD): 0.85, 95% confidence interval (95%CI): 0.54-1.16, I² = 86%, P < 0.001]. PGK1 expression was higher in 1520 TNBC than 7072 non-TNBC cases (SMD: 0.25, 95%CI: 0.03-0.47, I² = 91%, P = 0.02). Recurrence free survival was lower in PGK1-high-expression than PGK1-low-expression group (hazard ratio: 1.282, P = 0.023). PGK1 co-expressed genes were concentrated in ATP metabolic process, HIF-1 signaling, and glycolysis/gluconeogenesis pathways.

PGK1 expression is a research hotspot and frontier direction in the BC field. PGK1 may play a strong role in promoting cancer in TNBC by mediating metabolism and HIF-1 signaling pathways.

Long noncoding RNAs (lncRNAs) play a critical role in breast cancer pathogenesis, including Triple-Negative Breast Cancer (TNBC) subtype. Identifying the lncRNA expression patterns across different breast cancer subtypes could provide valuable insights into their potential utilization as disease biomarkers and therapeutic targets. In this study, we profiled lncRNA expression in 96 breast cancer cases, revealing significant differences compared to normal breast tissue. Variations across breast cancer subtypes, including Hormone Receptor-positive (HR + ), HER2-positive (HER2 + ), HER2 + HR + , and TNBC, as well as in relation to tumor grade and patients' age at diagnosis were observed. TNBC and HER2+ subtypes showed distinct clustering, while HER2 + HR+ tumors clustered closer to HR+ tumors based on their lncRNA profiles. Our data identified numerous enriched lncRNAs in TNBC, notably the elevated expression of LINC00960, which was subsequently validated in two additional datasets. Analysis of LINC00960 expression in an independent TNBC cohort (n = 360) revealed elevated expression of LINC00960 to correlate with cell movement, invasion, proliferation, and migration functional categories. Depletion of LINC00960 significantly reduced TNBC cell viability, colony formation, migration, and three-dimensional growth, while increasing cell death. Mechanistically, transcriptomic profiling of LINC00960-depleted cells confirmed its tumor-promoting role, likely through sponging of hsa-miR-34a-5p, hsa-miR-16-5p, and hsa-miR-183-5p, leading to the upregulation of cancer-promoting genes including BMI1, KRAS, and AKT3. Our findings highlight the distinct lncRNA expression patterns in breast cancer subtypes and underscore the crucial role for LINC00960 in promoting TNBC pathogenesis, suggesting its potential utilization as a prognostic marker and therapeutic target.

Approximately, 15% of global breast cancer cases are diagnosed as triple-negative breast cancer (TNBC), identified as the most aggressive subtype due to the simultaneous absence of estrogen receptor, progesterone receptor, and HER2. This characteristic renders TNBC highly aggressive and challenging to treat, as it excludes the use of effective drugs such as hormone therapy and anti-HER2 agents. In this review, we explore standard therapies and recent emerging approaches for TNBC, including PARP inhibitors, immune checkpoint inhibitors, PI3K/AKT pathway inhibitors, and cytotoxin-conjugated antibodies. The mechanism of action of these drugs and their utilization in clinical practice is explained in a pragmatic and prospective manner, contextualized within the current landscape of standard therapies for this pathology. These advancements present a promising frontier for tailored interventions with the potential to significantly improve outcomes for TNBC patients. Interestingly, while TNBC poses a complex challenge, it also serves as a paradigm and an opportunity for translational research and innovative therapies in the field of oncology.

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Previous studies have suggested that breast cancer (BC) from the Middle East and North Africa (MENA) is presented at younger age with advanced tumor stage, indicating underlying biological differences. Given the scant transcriptomic data on BC from the MENA region and to better understand the biology of this disease, we performed mRNA and microRNA (miRNA) transcriptomic profiling on a local cohort of BC (n = 96) from Qatar. Our data revealed the differentially expressed genes and miRNAs as function of BC molecular subtypes (HR⁺, HER2⁺, HER2⁺HR⁺, and TNBC), tumor grade (GIII vs GI-II), patients' age (young (≤40) vs old (>40)), and ethnicity (MENA vs non-MENA). Our profiling data revealed close similarity between TNBC and HER2⁺, while the transcriptome of HER2⁺HR⁺ tumor was resemblant of that from HR⁺ tumors. Network analysis identified complex miRNA-mRNA regulatory networks in each BC molecular subtype, in high vs low grade tumors, in tumors from young vs old patients, and in tumors from MENA vs non-MENA, thus implicating miRNA-mediated gene regulation as an essential mechanism in shaping the transcriptome of BC. Integration of our transcriptomic data with CRISPR-Cas9 functional screen data and the OncoKB database identified numerous dependencies and therapeutic vulnerabilities in each BC molecular subtype, while CDC123 was functionally validated as potential therapeutic target for TNBC. Cox regression survival analyses identified mRNA and miRNA-based signatures predicative of worse and better relapse free survival (RFS), which were validated in larger BC cohorts. Our data provides comprehensive transcriptomic profiling and unraveled the miRNA-mRNA regulatory networks in BC patients from the region and identified novel actionable gene targets, employing integrated approach. Findings from the current study have potential implications to improve the current standard-of-care for BC from the MENA as well as patients from other ethnicities.