Search Results (7)

Colorectal cancer remains one of the leading causes of cancer-related deaths globally. Non-protein coding RNAs, including microRNAs, have emerged as crucial regulators in cancer progression. Herein, we analyzed publicly available datasets for miRNA expression in healthy controls, adenomatous polyps, and colorectal cancer and identified their regulatory networks using HCT116 and HT-29 CRC models. Differentially expressed miRNAs in adenomatous polyps and colorectal cancer were identified, highlighting their role in colorectal cancer initiation and progression. Notably, miR-218-5p was significantly downregulated in adenomatous polyps and colorectal cancer, suggesting a role in colorectal cancer initiation. Functional investigations revealed a tumor suppressive role for miR-218-5p in HCT116 and HT-29 CRC cell models, affecting cell proliferation and three-dimensional organoid formation and promoting cell death. RNA-Seq and bioinformatics identified BIRC5 and DDX21 as bona fide gene targets for miR-218-5p, validated by reverse transcription quantitative PCR and Western blotting. Further investigation into the genomic location of miR-218-5p, embedded within the SLIT2 and SLIT3 introns on chromosome 4 and chromosome 5, respectively, revealed epigenetic silencing through promoter hypermethylation in colorectal cancer cell models. These findings highlight epigenetic silencing of miR-218-5p in colorectal cancer, suggesting its potential as a biomarker and therapeutic target for early detection and intervention. Full article

Background: 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. Methods: 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. Results: 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). Conclusions: 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. Full article

Breast cancer (BC) is a heterogeneous disease, which is primarily classified according to hormone receptors and HER2 expression. Despite the many advances in BC diagnosis and management, the identification of novel actionable therapeutic targets expressed by cancerous cells has always been a daunting task due to the large heterogeneity of the disease and the presence of non-cancerous cells (i.e., immune cells and stromal cells) within the tumor microenvironment. In the current study, we employed computational algorithms to decipher the cellular composition of estrogen receptor-positive (ER⁺), HER2⁺, ER⁺HER2⁺, and triple-negative BC (TNBC) subtypes from a total of 49,899 single cells’ publicly available transcriptomic data derived from 26 BC patients. Restricting the analysis to EPCAM⁺Lin⁻ tumor epithelial cells, we identified the enriched gene sets in each BC molecular subtype. Integration of single-cell transcriptomic with CRISPR-Cas9 functional screen data identified 13 potential therapeutic targets for ER⁺, 44 potential therapeutic targets for HER2⁺, and 29 potential therapeutic targets for TNBC. Interestingly, several of the identified therapeutic targets outperformed the current standard of care for each BC subtype. Given the aggressive nature and lack of targeted therapies for TNBC, elevated expression of ENO1, FDPS, CCT6A, TUBB2A, and PGK1 predicted worse relapse-free survival (RFS) in basal BC (n = 442), while elevated expression of ENO1, FDPS, CCT6A, and PGK1 was observed in the most aggressive BLIS TNBC subtype. Mechanistically, targeted depletion of ENO1 and FDPS halted TNBC cell proliferation, colony formation, and organoid tumor growth under 3-dimensional conditions and increased cell death, suggesting their potential use as novel therapeutic targets for TNBC. Differential expression and gene set enrichment analysis in TNBC revealed enrichment in the cycle and mitosis functional categories in FDPS^high, while ENO1^high was associated with numerous functional categories, including cell cycle, glycolysis, and ATP metabolic processes. Taken together, our data are the first to unravel the unique gene signatures and to identify novel dependencies and therapeutic vulnerabilities for each BC molecular subtype, thus setting the foundation for the future development of more effective targeted therapies for BC. Full article

Triple-negative breast cancer (TNBC) patients exhibiting pathological complete response (pCR) have better clinical outcomes compared to those with residual disease (RD). Therefore, robust biomarkers that can predict pCR may help with triage and resource prioritization in patients with TNBC. Herein, we identified a gene panel predictive of RD and pCR in TNBC from the discovery (n = 90) treatment-naive tumor transcriptomic data. Eight RD-derived genes were identified as TNBC-essential genes, which were highly predicative of overall survival (OS) and relapse-free survival (RFS) in an additional cohort of basal breast cancer (n = 442). Mechanistically, targeted depletion of the eight genes reduced the proliferation potential of TNBC cell models, while most remarkable effects were for combined SLC39A7, TIMM13, BANF1, and MVD knockdown in conjunction with doxorubicin. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and receiver operating characteristic curve (ROC) analyses revealed significant predictive power for the identified gene panels with an area under the curve (AUC) of 0.75 for the validation cohort (n = 50) to discriminate RD from pCR. Protein–Protein Interaction (PPI) network analysis of the pCR-derived gene signature identified an 87-immune gene signature highly predictive of pCR, which correlated with better OS, RFS, and distant-metastasis-free survival (DMFS) in an independent cohort of basal and, to a lesser extent, HER2+ breast cancer. Our data have identified gene signatures predicative of RD and pCR in TNBC with potential clinical implications. Full article

Triple negative breast cancer (TNBC) represents a diverse group of cancers based on their gene expression profiles. While the current mRNA-based classification of TNBC has contributed to our understanding of the heterogeneity of this disease, whether such heterogeneity can be resolved employing a long noncoding RNA (lncRNA) transcriptome has not been established thus far. Herein, we used iterative clustering and guide-gene selection (ICGS) and uniform manifold approximation and projection (UMAP) dimensionality reduction analysis on a large cohort of TNBC transcriptomic data (TNBC = 360, normal = 88) and classified TNBC into four main clusters: LINC00511-enriched, LINC00393-enriched, FIRRE-enriched, and normal tissue-like. Delving into associated gene expression profiles revealed remarkable differences in canonical, casual, upstream, and functional categories among different lncRNA-derived TNBC clusters, suggesting functional consequences for altered lncRNA expression. Correlation and survival analysis comparing mRNA- and lncRNA-based clustering revealed similarities and differences between the two classification approaches. To provide insight into the potential role of the identified lncRNAs in TNBC biology, CRISPR-Cas9 mediated LINC00511 promoter deletion reduced colony formation and enhanced the sensitivity of TNBC cells to paclitaxel, suggesting a role for LINC00511 in conferring tumorigenicity and resistance to therapy. Our data revealed a novel lncRNA-based classification of TNBC and suggested their potential utilization as disease biomarkers and therapeutic targets. Full article

Cumulative data link cytokine storms with coronavirus disease 2019 (COVID-19) severity. The precise identification of immune cell subsets in bronchoalveolar lavage (BAL) and their correlation with COVID-19 disease severity are currently being unraveled. Herein, we employed iterative clustering and guide-gene selection 2 (ICGS2) as well as uniform manifold approximation and projection (UMAP) dimensionality reduction computational algorithms to decipher the complex immune and cellular composition of BAL, using publicly available datasets from a total of 68,873 single cells derived from two healthy subjects, three patients with mild COVID-19, and five patients with severe COVID-19. Our analysis revealed the presence of neutrophils and macrophage cluster-1 as a hallmark of severe COVID-19. Among the identified gene signatures, IFITM2, IFITM1, H3F3B, SAT1, and S100A8 gene signatures were highly associated with neutrophils, while CCL8, CCL3, CCL2, KLF6, and SPP1 were associated with macrophage cluster-1 in severe-COVID-19 patients. Interestingly, although macrophages were also present in healthy subjects and patients with mild COVID-19, they had different gene signatures, indicative of interstitial and cluster-0 macrophage (i.e., FABP4, APOC1, APOE, C1QB, and NURP1). Additionally, MALAT1, NEAT1, and SNGH25 were downregulated in patients with mild and severe COVID-19. Interferon signaling, FCγ receptor-mediated phagocytosis, IL17, and Tec kinase canonical pathways were enriched in patients with severe COVID-19, while PD-1 and PDL-1 pathways were suppressed. A number of upstream regulators (IFNG, PRL, TLR7, PRL, TGM2, TLR9, IL1B, TNF, NFkB, IL1A, STAT3, CCL5, and others) were also enriched in BAL cells from severe COVID-19-affected patients compared to those from patients with mild COVID-19. Further analyses revealed genes associated with the inflammatory response and chemotaxis of myeloid cells, phagocytes, and granulocytes, among the top activated functional categories in BAL from severe COVID-19-affected patients. Transcriptome data from another cohort of COVID-19-derived peripheral blood mononuclear cells (PBMCs) revealed the presence of several genes common to those found in BAL from patients with severe and mild COVID-19 (IFI27, IFITM3, IFI6, IFIT3, MX1, IFIT1, OASL, IFI30, OAS1) or to those seen only in BAL from severe-COVID-19 patients (S100A8, IFI44, IFI44L, CXCL8, CCR1, PLSCR1, EPSTI1, FPR1, OAS2, OAS3, IL1RN, TYMP, BCL2A1). Taken together, our data reveal the presence of neutrophils and macrophage cluster-1 as the main immune cell subsets associated with severe COVID-19 and identify their inflammatory and chemotactic gene signatures, also partially reflected systemically in the circulation, for possible diagnostic and therapeutic interventions. Full article

The global spread of COVID-19, caused by pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for an imminent response from medical research communities to better understand this rapidly spreading infection. Employing multiple bioinformatics and computational pipelines on transcriptome data from primary normal human bronchial epithelial cells (NHBE) during SARS-CoV-2 infection revealed activation of several mechanistic networks, including those involved in immunoglobulin G (IgG) and interferon lambda (IFNL) in host cells. Induction of acute inflammatory response and activation of tumor necrosis factor (TNF) was prominent in SARS-CoV-2 infected NHBE cells. Additionally, disease and functional analysis employing ingenuity pathway analysis (IPA) revealed activation of functional categories related to cell death, while those associated with viral infection and replication were suppressed. Several interferon (IFN) responsive gene targets (IRF9, IFIT1, IFIT2, IFIT3, IFITM1, MX1, OAS2, OAS3, IFI44 and IFI44L) were highly upregulated in SARS-CoV-2 infected NBHE cell, implying activation of antiviral IFN innate response. Gene ontology and functional annotation of differently expressed genes in patient lung tissues with COVID-19 revealed activation of antiviral response as the hallmark. Mechanistic network analysis in IPA identified 14 common activated, and 9 common suppressed networks in patient tissue, as well as in the NHBE cell model, suggesting a plausible role for these upstream regulator networks in the pathogenesis of COVID-19. Our data revealed expression of several viral proteins in vitro and in patient-derived tissue, while several host-derived long noncoding RNAs (lncRNAs) were identified. Our data highlights activation of IFN response as the main hallmark associated with SARS-CoV-2 infection in vitro and in human, and identified several differentially expressed lncRNAs during the course of infection, which could serve as disease biomarkers, while their precise role in the host response to SARS-CoV-2 remains to be investigated. Full article