Single-cell RNA sequencing for the identification of early-stage lung cancer biomarkers from circulating blood

HMGA1 orchestrates chromatin compartmentalization and sequesters genes into 3D networks coordinating senescence heterogeneity

HMGA1 is an abundant non-histone chromatin protein that has been implicated in embryonic development, cancer, and cellular senescence, but its specific role remains elusive. Here, we combine functional genomics approaches with graph theory to investigate how HMGA1 genomic deposition controls high-order chromatin networks in an oncogene-induced senescence model. While the direct role of HMGA1 in gene activation has been described previously, we find little evidence to support this. Instead, we show that the heterogeneous linear distribution of HMGA1 drives a specific 3D chromatin organization. HMGA1-dense loci form highly interactive networks, similar to, but independent of, constitutive heterochromatic loci. This, coupled with the exclusion of HMGA1-poor chromatin regions, leads to coordinated gene regulation through the repositioning of genes. In the absence of HMGA1, the whole process is largely reversed, but many regulatory interactions also emerge, amplifying the inflammatory senescence-associated secretory phenotype. Such HMGA1-mediated fine-tuning of gene expression contributes to the heterogeneous nature of senescence at the single-cell level. A similar 'buffer' effect of HMGA1 on inflammatory signalling is also detected in lung cancer cells. Our study reveals a mechanism through which HMGA1 modulates chromatin compartmentalization and gene regulation in senescence and beyond.

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Elucidating the cellular and genetic composition of ocular tissues is essential for uncovering the pathophysiology of ocular diseases. Since the introduction of single-cell RNA sequencing (scRNA-seq) in 2009, vision researchers have performed extensive single-cell analyses to better understand transcriptome complexity and heterogeneity of ocular structures. This technology has revolutionized our ability to identify rare cell populations and to make cross-species comparisons of gene expression in both steady state and disease conditions. Importantly, single-cell transcriptomic analyses have enabled the identification of cell-type specific gene markers and signalling pathways between ocular cell populations. While most scRNA-seq studies have been conducted on retinal tissues, large-scale transcriptomic atlases pertaining to the ocular anterior segment have also been constructed in the past three years. This timely review provides vision researchers with an overview of scRNA-seq experimental design, technical limitations, and clinical applications in a variety of anterior segment-related ocular pathologies. We review open-access anterior segment-related scRNA-seq datasets and illustrate how scRNA-seq can be an indispensable tool for the development of targeted therapeutics.

Polo-like kinase 1 suppresses lung adenocarcinoma immunity through necroptosis

Polo-like kinase 1 (PLK1) plays a crucial role in cell mitosis and has been associated with necroptosis. However, the role of PLK1 and necroptosis in lung adenocarcinoma (LA) remains unclear. In this study, we analyzed The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression databases to evaluate the prognostic value and mechanistic role of PLK1 in LA. PLK1 was found to be highly expressed in LA and was positively associated with advanced disease staging and poor survival outcomes. Functional enrichment analysis showed that PLK1 was involved in cell mitosis, neurotransmitter transmission, and drug metabolism. Further analysis using single-sample gene set enrichment analysis and ESTIMATE algorithm revealed a correlation between PLK1 expression and immune infiltration in LA. Silencing of PLK1 using miRNA transfection in LA cells reduced cell proliferation and increased apoptosis, as well as upregulating the expression of necroptosis-related proteins, such as RIPK1, RIPK3, and MLKL. Additionally, nude mouse transplantation tumor experiments demonstrated that silencing PLK1 reduced the growth capacity of LA cells. These findings suggest that PLK1 plays a critical role in LA progression by regulating necroptosis and immune infiltration, and may serve as a potential therapeutic target for immunotherapy. Furthermore, PLK1 expression can be used as a prognostic biomarker for LA patients.

"SMART" cytology: The next generation cytology for precision diagnosis

Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Cellular and Molecular Profiling of Tumor Microenvironment and Early-Stage Lung Cancer

Lung cancers are broadly divided into two categories: non-small-cell lung carcinoma (NSCLC), which accounts for 80-85% of all cancer cases, and small-cell lung carcinoma (SCLC), which covers the remaining 10-15%. Recent advances in cancer biology and genomics research have allowed an in-depth characterization of lung cancers that have revealed new therapy targets (EGFR, ALK, ROS, and KRAS mutations) and have the potential of revealing even more biomarkers for diagnostic, prognostic, and targeted therapies. A new source of biomarkers is represented by non-coding RNAs, especially microRNAs (miRNAs). MiRNAs are short non-coding RNA sequences that have essential regulatory roles in multiple cancers. Therefore, we aim to investigate the tumor microenvironment (TME) and miRNA tumor profile in a subset of 51 early-stage lung cancer samples (T1 and T2) to better understand early tumor and TME organization and molecular dysregulation. We analyzed the immunohistochemistry expression of CD4 and CD8 as markers of the main TME immune populations, E-cadherin to evaluate early-stage epithelial-to-mesenchymal transition (EMT), and p53, the main altered tumor suppressor gene in lung cancer. Starting from these 4 markers, we identified and validated 4 miRNAs that target TP53 and regulate EMT that can be further investigated as potential early-stage lung cancer biomarkers.

The Role of Myeloid Cells in Hepatotoxicity Related to Cancer Immunotherapy

Drug-related hepatotoxicity is an emerging clinical challenge with the widening use of immunotherapeutic agents in the field of oncology. This is an important complication to consider as more immune oncological targets are being identified to show promising results in clinical trials. The application of these therapeutics may be complicated by the development of immune-related adverse events (irAEs), a serious limitation often requiring high-dose immunosuppression and discontinuation of cancer therapy. Hepatoxicity presents one of the most frequently encountered irAEs and a better understanding of the underlying mechanism is crucial for the development of alternative therapeutic interventions. As a novel drug side effect, the immunopathogenesis of the condition is not completely understood. In the liver, myeloid cells play a central role in the maintenance of homeostasis and promotion of inflammation. Recent research has identified myeloid cells to be associated with hepatic adverse events of various immune modulatory monoclonal antibodies. In this review article, we provide an overview of the role of myeloid cells in the immune pathogenesis during hepatoxicity related to cancer immunotherapies and highlight potential treatment options.

The Effect of Beta Adrenoreceptor Blockers on Viability and Cell Colony Formation of Non-Small Cell Lung Cancer Cell Lines A549 and H1299

Beta adrenoblockers are a large class of drugs used to treat cardiovascular diseases, migraines, glaucoma and hyperthyroidism. Over the last couple of decades, the anticancer effects of these compounds have been extensively studied. However, the exact mechanism is still not known, and more detailed studies are required. The aim of our study was to evaluate the anticancer activity of beta adrenoblockers in non-small cell lung cancer cell lines A549 and H1299. In order to find the relationship with their selectivity to beta adrenoreceptors, selective (atenolol, betaxolol, esmolol, metoprolol) and non-selective (pindolol, propranolol and timolol) beta blockers were tested. The effect on cell viability was evaluated by MTT assay, and the activity on cell ability to form colonies was tested by clonogenic assay. The type of cell death was evaluated by cell double staining with Hoechst 33342 and Propidium iodide. The most active adrenoblockers against both tested cancer cell lines were propranolol and betaxolol. They completely inhibited lung cancer cell colony formation at 90% of the EC₅₀ (half-maximal effective concentration) value. Most tested compounds induced cell death through apoptosis and necrosis. There was no correlation established between beta adrenoblocker anticancer activity and their selectivity to beta adrenoreceptors.

Single-Cell RNA Sequencing Analysis Using Fluidigm C1 Platform for Characterization of Heterogeneous Transcriptomes

Single-cell RNA sequencing (scRNA-seq) is an innovative technology that can be used to characterize transcriptome heterogeneity at the single-cell resolution. The Fluidigm C1 is an automated system for preparing complementary deoxyribonucleic acid (cDNA) samples that have been synthesized and preamplified from single cells for next-generation sequencing (NGS) analysis. Herein, we detail a workflow and protocol for scRNA-seq analysis of 3'-end enriched cDNA libraries and introduce strategies for validation of differential gene expression.