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In this study, the secondary sequencing was used to profile circRNA expression in the tissue samples from three CRC patients with liver metastasis and three matched CRC patients. After verified some candidates in another 40 CRC and CRC-m samples by qRT-PCR, we further demonstrated that circRNA_0001178 and circRNA_0000826 were significantly upregulated in CRC-m tissues, and both of them had the potential for diagnosing liver metastases from colorectal cancer. Finally, the networks of circRNA-miRNA-mRNA base on these two circRNAs were constructed respectively. This study showed that differentially expressed circRNAs were existed between the tissue samples from colorectal cancer patients with and without liver metastasis. And also suggested that circRNA_0001178 and circRNA_0000826 may serve as a potential diagnostic biomarker for liver metastases from colorectal cancer.

Background The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. Results We develop DrImpute to impute dropout events in scRNA-seq data. We show that DrImpute has significantly better performance on the separation of the dropout zeros from true zeros than existing imputation algorithms. We also demonstrate that DrImpute can significantly improve the performance of existing tools for clustering, visualization and lineage reconstruction of nine published scRNA-seq datasets. Conclusions DrImpute can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute is implemented in R and is available at https://github.com/gongx030/DrImpute .

Despite marked development in cancer therapies during recent decades, the prognosis for advanced cancer remains poor. The conventional tumor–cell‐centric view of cancer can only explain part of cancer progression, and thus a thorough understanding of the tumor microenvironment (TME) is crucial. Among cells within the TME, cancer‐associated fibroblasts (CAFs) are attracting attention as a target for cancer therapy. However, CAFs present a heterogeneous population of cells and more detailed classification of CAFs and investigation of functions of each subset is needed to develop novel CAF‐targeted therapies. In this context, application of newly developed approaches to single‐cell analysis has already made an impact on our understanding of the heterogeneity of CAFs. Here, we review the recent literature on CAF heterogeneity and function, and discuss the possibility of novel therapies targeting CAF subsets. The conventional tumor–cell‐centric view of cancer can only explain a part of cancer progression, thus understanding of the tumor microenvironment (TME) is crucial. Among cells within the TME, cancer‐associated fibroblasts (CAFs) are attracting attention as a target for cancer therapy. Here, we review the recent literature that has improved our understanding of heterogeneity in CAFs and function of each subset, and discuss the possibility of novel therapies targeting CAF subsets.

Background Next-Generation Sequencing (NGS) catalyzed breakthroughs across various scientific domains. Illumina’s sequencing by synthesis method has long been central to NGS, but new sequencing methods like Element Biosciences’ AVITI technology are emerging. AVITI is reported to offer improved signal-to-noise ratios and cost reductions. However, its reliance on rolling circle amplification, which can be affected by polymer size, raises questions about its effectiveness in sequencing small RNAs (sRNAs) such as microRNAs (miRNAs), small nucleolar RNAs (snoRNAs), and many others. These sRNAs are crucial regulators of gene expression and involved in various biological processes. Additionally, capturing capped small RNAs (csRNA-seq) is a powerful method for mapping active or “nascent” RNA polymerase II transcription initiation in tissues and clinical samples. Results Here, we report a new protocol for seamlessly sequencing short fragments on the AVITI and demonstrate that AVITI and Illumina sequencing technologies equivalently capture human, cattle ( Bos taurus ), and bison ( Bison bison ) sRNA or csRNA sequencing libraries, increasing confidence in both sequencing approaches. Additionally, analysis of generated nascent transcription start site (TSS) data for cattle and bison revealed inaccuracies in their current genome annotations, underscoring the potential and necessity to translate small and nascent RNA sequencing methodologies to livestock. Conclusions Our accelerated and optimized protocol bridges the advantages of AVITI sequencing with critical methods that rely on sequencing short fragments. This advance bolsters the utility of AVITI technology alongside traditional Illumina platforms, offering new opportunities for NGS applications.

Acute myeloid leukemia (AML) is an aggressive malignancy that poses significant challenges due to high rates of relapse and resistance to treatment, particularly in older populations. While therapeutic advances have been made, survival outcomes remain suboptimal. The evolution of DNA and RNA sequencing technologies, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-Seq), has significantly enhanced our understanding of AML at the molecular level. These technologies have led to the discovery of driver mutations and transcriptomic alterations critical for improving diagnosis, prognosis, and personalized therapy development. Furthermore, single-cell RNA sequencing (scRNA-Seq) has uncovered rare subpopulations of leukemia stem cells (LSCs) contributing to disease progression and relapse. However, widespread clinical integration of these tools remains limited by costs, data complexity, and ethical challenges. This review explores recent advancements in DNA/RNA sequencing in AML and highlights both the potential and limitations of these techniques in clinical practice.

Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.

Extracting dynamical information from single‐cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory‐oriented, bottom‐up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single‐cell RNA sequencing (scRNA‐seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state‐specific gene–gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre‐existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions. Synopsis spliceJAC builds a multivariate mRNA splicing model from single‐cell transcriptome data to infer the context‐specific gene regulation and the key driver genes that guide the transition between cell states. spliceJAC constructs cell state‐specific gene regulatory networks and quantifies changes in signaling roles between cell states. spliceJAC employs stability analysis to identify driver genes that guide transitions between cell states. Context‐specific gene regulation and transition genes are identified using spliceJAC during pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells. Graphical Abstract spliceJAC builds a multivariate mRNA splicing model from single‐cell transcriptome data to infer the context‐specific gene regulation and the key driver genes that guide the transition between cell states.

Cancer-associated fibroblasts (CAFs) are a heterogeneous cell population that plays a crucial role in remodeling the tumor microenvironment (TME). Here, through the integrated analysis of spatial and single-cell transcriptomics data across six common cancer types, we identified four distinct functional subgroups of CAFs and described their spatial distribution characteristics. Additionally, the analysis of single-cell RNA sequencing (scRNA-seq) data from three additional common cancer types and two newly generated scRNA-seq datasets of rare cancer types, namely epithelial-myoepithelial carcinoma (EMC) and mucoepidermoid carcinoma (MEC), expanded our understanding of CAF heterogeneity. Cell–cell interaction analysis conducted within the spatial context highlighted the pivotal roles of matrix CAFs (mCAFs) in tumor angiogenesis and inflammatory CAFs (iCAFs) in shaping the immunosuppressive microenvironment. In patients with breast cancer (BRCA) undergoing anti-PD-1 immunotherapy, iCAFs demonstrated heightened capacity in facilitating cancer cell proliferation, promoting epithelial-mesenchymal transition (EMT), and contributing to the establishment of an immunosuppressive microenvironment. Furthermore, a scoring system based on iCAFs showed a significant correlation with immune therapy response in melanoma patients. Lastly, we provided a web interface ( https://chenxisd.shinyapps.io/pancaf/ ) for the research community to investigate CAFs in the context of pan-cancer.

Integrin‐based focal adhesion is one of the major mechanosensory in osteocytes. The aim of this study was to mine the hub genes associated with focal adhesion and investigate their roles in osteoporosis based on the data of single‐cell RNA sequencing and RNA‐sequencing. Two hub genes (FAM129A and RNF24) with the same expression trend and AUC values greater than 0.7 in both GSE56815 and GSE56116 cohorts were uncovered. The nomogram was created to predict the risk of OP based on two hub genes. Subsequently, the competing endogenous RNA network was established based on two hub genes, 14 microRNAs and five long noncoding RNAs. Meanwhile, transcription factors‐hub gene network was established based on two hub genes and 14 TFs. Finally, 73 drugs were predicted, of which there were 13 drugs targeting FAM129A and 66 drugs targeting RNF24. In both mouse and human blood samples, FAM129A expression was decreased in granulocytes and RNF24 expression was increased in monocytes. In the mouse experiment, FAM129A and anti‐RNF24 were found to partially alleviate the progression of osteoporosis. In conclusion, two hub genes related to focal adhesion were identified by combined scRNA‐seq and RNA‐seq analyses, which might supply a new insight for the treatment and evaluation of OP.

Background The human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise. Results We developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA’s genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models. Conclusions Long read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.