spaCI: deciphering spatial cellular communications through adaptive graph model

Structural Systems Biology Toolkit (SSBtoolkit): From Molecular Structure to Subcellular Signaling Pathways

Here, we introduce the Structural Systems Biology (SSB) toolkit, a Python library that integrates structural macromolecular data with systems biology simulations to model signal-transduction pathways of G-protein-coupled receptors (GPCRs). Our framework streamlines simulation and analysis of the mathematical models of GPCRs cellular pathways, facilitating the exploration of the signal-transduction kinetics induced by ligand-GPCR interactions: the dose-response of the ligand can be modeled, along with the corresponding change in the concentration of other signaling molecular species over time, like for instance [Ca2+] or [cAMP]. SSB toolkit brings to light the possibility of easily investigating the subcellular effects of ligand binding on receptor activation, even in the presence of genetic mutations, thereby enhancing our understanding of the intricate relationship between ligand-target interactions at the molecular level and the higher-level cellular and (patho)physiological response mechanisms.

New Insights and Implications of Cell-Cell Interactions in Developmental Biology

The dynamic and meticulously regulated networks established the foundation for embryonic development, where the intercellular interactions and signal transduction assumed a pivotal role. In recent years, high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced dramatically, empowering the systematic dissection of cell-to-cell regulatory networks. The emergence of comprehensive databases and analytical frameworks has further provided unprecedented insights into embryonic development and cell-cell interactions (CCIs). This paper reviewed the exponential increased CCIs works related to developmental biology from 2008 to 2023, comprehensively collected and categorized 93 analytical tools and 39 databases, and demonstrated its practical utility through illustrative case studies. In parallel, the article critically scrutinized the persistent challenges within this field, such as the intricacies of spatial localization and transmembrane state validation at single-cell resolution, and underscored the interpretative limitations inherent in current analytical frameworks. The development of CCIs' analysis tools with harmonizing multi-omics data and the construction of cross-species dynamically updated CCIs databases will be the main direction of future research. Future investigations into CCIs are poised to expeditiously drive the application and clinical translation within developmental biology, unlocking novel dimensions for exploration and progress.

Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data with miRTalk

MicroRNAs are released from cells in extracellular vesicles (EVs), representing an essential mode of cell-cell communication (CCC) via a regulatory effect on gene expression. Single-cell RNA-sequencing technologies have ushered in an era of elucidating CCC at single-cell resolution. Herein, we present miRTalk, a pioneering approach for inferring CCC mediated by EV-derived miRNA-target interactions (MiTIs). The benchmarking against simulated and real-world datasets demonstrates the superior performance of miRTalk, and the application to four disease scenarios reveals the in-depth MiTI-mediated CCC mechanisms. Collectively, miRTalk can infer EV-derived MiTI-mediated CCC with scRNA-seq data, providing new insights into the intercellular dynamics of biological processes.

Graph neural networks for single-cell omics data: a review of approaches and applications

Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular signaling molecules within single-cell datasets, coupled with the complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges to conventional deep learning operations reliant on Euclidean geometries. Graph neural networks (GNNs) have extended deep learning to non-Euclidean data, allowing cells and their features in single-cell datasets to be modeled as nodes within a graph structure. GNNs have been successfully applied across a broad range of tasks in single-cell data analysis. In this survey, we systematically review 107 successful applications of GNNs and their six variants in various single-cell omics tasks. We begin by outlining the fundamental principles of GNNs and their six variants, followed by a systematic review of GNN-based models applied in single-cell epigenomics, transcriptomics, spatial transcriptomics, proteomics, and multi-omics. In each section dedicated to a specific omics type, we have summarized the publicly available single-cell datasets commonly utilized in the articles reviewed in that section, totaling 77 datasets. Finally, we summarize the potential shortcomings of current research and explore directions for future studies. We anticipate that this review will serve as a guiding resource for researchers to deepen the application of GNNs in single-cell omics.

Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer

To unravel the mechanism of immune activation and suppression within tumors, a critical step is to identify transcriptional signals governing cell-cell communication between tumor and immune/stromal cells in the tumor microenvironment. Central to this communication are interactions between secreted ligands and cell-surface receptors, creating a highly connected signaling network among cells. Recent advancements in in situ-omics profiling, particularly spatial transcriptomic (ST) technology, provide unique opportunities to directly characterize ligand-receptor signaling networks that power cell-cell communication. In this paper, we propose a novel statistical method, LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and immune/stroma cells based on ST data. LRnetST utilizes a directed acyclic graph model with a novel approach to handle the zero-inflated distributions of ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. Application of LRnetST to ST data of high-grade serous ovarian tumor samples revealed both common and distinct ligand-receptor regulations across different tumors. Some of these interactions were validated through both a MERFISH dataset and a CosMx SMI dataset of independent ovarian tumor samples. These results cast light on biological processes relating to the communication between tumor and immune/stromal cells in ovarian tumors. An open-source R package of LRnetST is available on GitHub at https://github.com/jie108/LRnetST.

SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell-cell communication

BACKGROUND

Cellular communication is vital for the proper functioning of multicellular organisms. A comprehensive analysis of cellular communication demands the consideration not only of the binding between ligands and receptors but also of a series of downstream signal transduction reactions within cells. Thanks to the advancements in spatial transcriptomics technology, we are now able to better decipher the process of cellular communication within the cellular microenvironment. Nevertheless, the majority of existing spatial cell-cell communication algorithms fail to take into account the downstream signals within cells.

RESULTS

In this study, we put forward SpaCcLink, a cell-cell communication analysis method that takes into account the downstream influence of individual receptors within cells and systematically investigates the spatial patterns of communication as well as downstream signal networks. Analyses conducted on real datasets derived from humans and mice have demonstrated that SpaCcLink can help in identifying more relevant ligands and receptors, thereby enabling us to systematically decode the downstream genes and signaling pathways that are influenced by cell-cell communication. Comparisons with other methods suggest that SpaCcLink can identify downstream genes that are more closely associated with biological processes and can also discover reliable ligand-receptor relationships.

CONCLUSIONS

By means of SpaCcLink, a more profound and all-encompassing comprehension of the mechanisms underlying cellular communication can be achieved, which in turn promotes and deepens our understanding of the intricate complexity within organisms.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Detecting global and local hierarchical structures in cell-cell communication using CrossChat

Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat's functionalities for analyzing both global and local hierarchical structures within cell-cell communication.

A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

Spatial transcriptomics technologies enable researchers to accurately quantify and localize messenger ribonucleic acid (mRNA) transcripts at a high resolution while preserving their spatial context. The identification of spatial domains, or the task of spatial clustering, plays a crucial role in investigating data on spatial transcriptomes. One promising approach for classifying spatial domains involves the use of graph neural networks (GNNs) by leveraging gene expressions, spatial locations, and histological images. This study provided a comprehensive overview of the most recent GNN-based methods of spatial clustering methods for the analysis of data on spatial transcriptomics. We extensively evaluated the performance of current methods on prevalent datasets of spatial transcriptomics by considering their accuracy of clustering, robustness, data stabilization, relevant requirements, computational efficiency, and memory use. To this end, we explored 60 clustering scenarios by extending the essential frameworks of spatial clustering for the selection of the GNNs, algorithms of downstream clustering, principal component analysis (PCA)-based reduction, and refined methods of correction. We comparatively analyzed the performance of the methods in terms of spatial clustering to identify their limitations and outline future directions of research in the field. Our survey yielded novel insights, and provided motivation for further investigating spatial transcriptomics.

THGB: predicting ligand-receptor interactions by combining tree boosting and histogram-based gradient boosting

Ligand-receptor interaction (LRI) prediction has great significance in biological and medical research and facilitates to infer and analyze cell-to-cell communication. However, wet experiments for new LRI discovery are costly and time-consuming. Here, we propose a computational model called THGB to uncover new LRIs. THGB first extracts feature information of Ligand-Receptor (LR) pairs using iFeature. Next, it adopts a tree boosting model to obtain representative LR features. Finally, it devises the histogram-based gradient boosting model to capture high-quality LRIs. To assess the THGB performance, we compared it with three new LRI prediction models (i.e., CellEnBoost, CellGiQ, and CellComNet) and one classical protein-protein interaction inference model PIPR. The results demonstrated that THGB achieved the best overall predictions in terms of six evaluation indictors (i.e., precision, recall, accuracy, F1-score, AUC, and AUPR). To measure the effect of LR feature selection on the prediction, THGB was compared with four feature selection methods (i.e., PCA, NMF, LLE, and TSVD). The results showed that the tree boosting model was more appropriate to select representative LR features and improve LRI prediction. We also conducted ablation study and found that THGB with feature selection outperformed THGB without feature selection. We hope that THGB is a useful tool to find new LRIs and further infer cell-to-cell communication.

Gene expression prediction from histology images via hypergraph neural networks

Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.

Cross-expression analysis reveals patterns of coordinated gene expression in spatial transcriptomics

Spatial transcriptomics promises to transform our understanding of tissue biology by molecularly profiling individual cells in situ. A fundamental question they allow us to ask is how nearby cells orchestrate their gene expression. To investigate this, we introduce cross-expression, a novel framework for discovering gene pairs that coordinate their expression across neighboring cells. Just as co-expression quantifies synchronized gene expression within the same cells, cross-expression measures coordinated gene expression between spatially adjacent cells, allowing us to understand tissue gene expression programs with single cell resolution. Using this framework, we recover ligand-receptor partners and discover gene combinations marking anatomical regions. More generally, we create cross-expression networks to find gene modules with orchestrated expression patterns. Finally, we provide an efficient R package to facilitate cross-expression analysis, quantify effect sizes, and generate novel visualizations to better understand spatial gene expression programs.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Gastric cancer immunosuppressive microenvironment heterogeneity: implications for therapy development

Although immunotherapy has revolutionized solid tumor treatment, durable responses in gastric cancer (GC) remain limited. The heterogeneous tumor microenvironment (TME) facilitates immune evasion, contributing to resistance to conventional and immune therapies. Recent studies have highlighted how specific TME components in GC acquire immune escape capabilities through cancer-specific factors. Understanding the underlying molecular mechanisms and targeting the immunosuppressive TME will enhance immunotherapy efficacy and patient outcomes. This review summarizes recent advances in GC TME research and explores the role of the immune-suppressive system as a context-specific determinant. We also provide insights into potential treatments beyond checkpoint inhibition.

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

The diversification of methods for studying cell-cell interactions and communication

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

T-reg transcriptomic signatures identify response to check-point inhibitors

Regulatory T cells (Tregs) is a subtype of CD4+ T cells that produce an inhibitory action against effector cells. In the present work we interrogated genomic datasets to explore the transcriptomic profile of breast tumors with high expression of Tregs. Only 0.5% of the total transcriptome correlated with the presence of Tregs and only four transcripts, BIRC6, MAP3K2, USP4 and SMG1, were commonly shared among the different breast cancer subtypes. The combination of these genes predicted favorable outcome, and better prognosis in patients treated with checkpoint inhibitors. Twelve up-regulated genes coded for proteins expressed at the cell membrane that included functions related to neutrophil activation and regulation of macrophages. A positive association between MSR1 and CD80 with macrophages in basal-like tumors and between OLR1, ABCA1, ITGAV, CLEC5A and CD80 and macrophages in HER2 positive tumors was observed. Expression of some of the identified genes correlated with favorable outcome and response to checkpoint inhibitors: MSR1, CD80, OLR1, ABCA1, TMEM245, and ATP13A3 predicted outcome to anti PD(L)1 therapies, and MSR1, CD80, OLR1, ANO6, ABCA1, TMEM245, and ATP13A3 to anti CTLA4 therapies, including a subgroup of melanoma treated patients. In this article we provide evidence of genes strongly associated with the presence of Tregs that modulates the response to check point inhibitors.

xSiGra: Explainable model for single-cell spatial data elucidation

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multi-channel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multi-modal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of Grad-CAM component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within the complex cellular communications.

Explore & actuate: the future of personalized medicine in oncology through emerging technologies

PURPOSE OF REVIEW

The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings.

RECENT FINDINGS

To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation.

SUMMARY

The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.

Computational cell-cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment

Complex networks of cell-cell interactions (CCIs) within the tumor microenvironment (TME) play a crucial role in cancer persistence. These communication axes represent prime targets for therapeutic intervention, but our incomplete understanding of the cellular heterogeneity and interacting partners within the TME remains a stubborn barrier to complete drug responses. This review outlines recent advances in the study of CCIs that leverage single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies that can clarify TME dynamics. We anticipate that these strategies will promote discovery of CCIs critical to the tumor-immune interface and will, by extension, expand the repertoire of druggable tumor biomarkers.

The microRNA target site profile is a novel biomarker in the immunotherapy response

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

LncRNA UCA1 promotes pancreatic cancer cell migration by regulating mitochondrial dynamics via the MAPK pathway

PURPOSE

Long non-coding RNA urothelial cancer associated 1 (UCA1) serves as an oncogene in various cancers. However, the mechanism underlying the role of UCA1 in pancreatic cancer remains unclear. This study aimed to explore the role of UCA1 in pancreatic cancer.

METHODS

The expression and prognosis of UCA1 were analyzed using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The results were validated by immunohistochemistry (IHC) and qRT-PCR. The biofunctions of UCA1 were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The migration abilities and mitochondrial dynamics of PC cells were examined using the Transwell assay, mitochondrial membrane potential (MMP), and fluorescence. The mitochondrial-related protein and MAPK/ERK pathway markers were evaluated using western blotting.

RESULTS

UCA1 expression was significantly higher in pancreatic cancer tissues than in normal tissues. High UCA1 expression indicated poor clinical outcomes and was associated with clinical features in patients with pancreatic cancer. Additionally, high UCA1 expression is a potential independent marker for poor prognosis. Subsequently, we demonstrated that UCA1 enhanced the migration capability, increased MMP, enhanced mitochondrial fusion, and inhibited mitochondrial autophagy in pancreatic cancer cells via the MAPK/ERK pathway.

CONCLUSION

UCA1 promotes the migration by regulating the mitochondrial dynamics of pancreatic cancer cells via the MAPK/ERK pathway. Our findings suggest that UCA1 may serve as a potential biomarker in pancreatic cancer prognosis.

Brain Metastases from Breast Cancer Histologically Exhibit Solid Growth Pattern with at Least Focal Comedonecrosis: A Histopathologic Study on a Monocentric Series of 30 Cases

Since there are no morphological clues capable of making a pathologist suspect a possible mammary origin of a metastatic lesion without adequate clinical information, the histologic diagnosis of brain metastasis from BC is still based on the immunohistochemical expression of mammary gland markers such as GATA-3, ERs, PgRs and HER-2. The present retrospective study aimed to select purely morphological features capable of suggesting the mammary origin of a metastatic carcinoma in the brain. The following histological features were collected from a series of 30 cases of brain metastases from breast cancer: (i) a solid growth pattern; (ii) the presence of comedonecrosis; and (iii) glandular differentiation. Our results showed that most cases histologically exhibited a solid growth pattern with at least focal comedonecrosis, producing an overall morphology closely reminiscent of mammary high-grade ductal carcinoma in situ. Although the above-mentioned morphological parameters are not strictly specific to a mammary origin, they may have an important diagnostic utility for leading pathologists to suspect a possible breast primary tumor and to include GATA-3, ERs, PgRs and HER-2 in the immunohistochemical panel.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Background

Sperm-associated antigen 5 (SPAG5) has been identified as a novel driver oncogene involved in multiple cancers; however, its role in lung adenocarcinoma (LUAD) needs further investigation. Our study aims to elucidate the potential significance of SPAG5 in LUAD prognosis and its implications for the efficacy of immunotherapy.

Methods

In this study, we used bioinformatics analysis and tissue microarray (TMA) staining to examine the potential role of SPAG5 in LUAD survival and response to immunotherapy. We used the Oncomine, TIMER2.0, Gene Expression Profiling Interactive Analysis (GEPIA), Sangerbox, PredicScan, and Kaplan-Meier Plotter databases to examine the expression and prognostic role of SPAG5 in the LUAD of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases. We also used Cancer Single-cell State Atlas (CancerSEA) and Tumor Immune Estimation Resource (TIMER2.0) to analyze the association of SPAG5 with malignant phenotype and tumor immune microenvironment. Furthermore, Immune Cell Abundance Identifier (ImmuCellAI) analysis of TCGA sequencing data was used to predict the role of SPAG5 in determining the response to immune checkpoint blockade (ICB) treatment in LUAD. Co-expression analysis of programmed death-ligand 1 (PD-L1) and SPAG5 was performed using LUAD TMA immunohistochemistry (IHC) analysis.

Results

Our findings indicate that SPAG5 is overexpressed in LUAD and is positively correlated with advanced clinical stage, poor overall survival, relapse-free survival, and progression-free survival outcomes. SPAG5 may be involved in regulating the cell cycle, proliferation, invasion, DNA damage and repair, and tumor immunosuppression. Furthermore, TMA IHC analysis showed a positive correlation between PD-L1 expression in LUAD and SPAG5 which suggests that SPAG5 may serve as a potential predictor of response to ICB therapy in LUAD.

Conclusions

Our results highlight the role of SAPG5 in promoting a tumor malignancy phenotype and immunosuppression in LUAD and suggest that SPAG5 may serve as a potential response marker for ICB therapy.

Multi-omics analysis and validation of the tumor microenvironment of hepatocellular carcinoma under RNA modification patterns

BACKGROUND

Multiple types of RNA modifications are associated with the prognosis of hepatocellular carcinoma (HCC) patients. However, the overall mediating effect of RNA modifications on the tumor microenvironment (TME) and the prognosis of patients with HCC is unclear.

METHODS

Thoroughly analyze the TME, biological processes, immune infiltration and patient prognosis based on RNA modification patterns and gene patterns. Construct a prognostic model (RNA modification score, RNAM-S) to predict the overall survival (OS) in HCC patients. Analyze the immune status, cancer stem cell (CSC), mutations and drug sensitivity of HCC patients in both the high and low RNAM-S groups. Verify the expression levels of the four characteristic genes of the prognostic RNAM-S using in vitro cell experiments.

RESULTS

Two modification patterns and two gene patterns were identified in this study. Both the high-expression modification pattern and the gene pattern exhibited worse OS. A prognostic RNAM-S model was constructed based on four featured genes (KIF20A, NR1I2, NR2F1 and PLOD2). Cellular experiments suggested significant dysregulation of the expression levels of these four genes. In addition, validation of the RNAM-S model using each data set showed good predictive performance of the model. The two groups of HCC patients (high and low RNAM-S groups) exhibited significant differences in immune status, CSC, mutation and drug sensitivity.

CONCLUSION

The findings of the study demonstrate the clinical value of RNA modifications, which provide new insights into the individualized treatment for patients with HCC.

Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

Adult tissue-specific stem cell interaction: novel technologies and research advances

Adult tissue-specific stem cells play a dominant role in tissue homeostasis and regeneration. Various in vivo markers of adult tissue-specific stem cells have been increasingly reported by lineage tracing in genetic mouse models, indicating that marked cells differentiation is crucial during homeostasis and regeneration. How adult tissue-specific stem cells with indicated markers contact the adjacent lineage with indicated markers is of significance to be studied. Novel methods bring future findings. Recent advances in lineage tracing, synthetic receptor systems, proximity labeling, and transcriptomics have enabled easier and more accurate cell behavior visualization and qualitative and quantitative analysis of cell-cell interactions than ever before. These technological innovations have prompted researchers to re-evaluate previous experimental results, providing increasingly compelling experimental results for understanding the mechanisms of cell-cell interactions. This review aimed to describe the recent methodological advances of dual enzyme lineage tracing system, the synthetic receptor system, proximity labeling, single-cell RNA sequencing and spatial transcriptomics in the study of adult tissue-specific stem cells interactions. An enhanced understanding of the mechanisms of adult tissue-specific stem cells interaction is important for tissue regeneration and maintenance of homeostasis in organisms.

Elevated ADAR expression is significantly linked to shorter overall survival and immune infiltration in patients with lung adenocarcinoma

To date, few studies have investigated whether the RNA-editing enzymes adenosine deaminases acting on RNA (ADARs) influence RNA functioning in lung adenocarcinoma (LUAD). To investigate the role of ADAR in lung cancer, we leveraged the advantages of The Cancer Genome Atlas (TCGA) database, from which we obtained transcriptome data and clinical information from 539 patients with LUAD. First, we compared ARAR expression levels in LUAD tissues with those in normal lung tissues using paired and unpaired analyses. Next, we evaluated the influence of ADARs on multiple prognostic indicators, including overall survival at 1, 3 and 5 years, as well as disease-specific survival and progression-free interval, in patients with LUAD. We also used Kaplan-Meier survival curves to estimate overall survival and Cox regression analysis to assess covariates associated with prognosis. A nomogram was constructed to validate the impact of the ADARs and clinicopathological factors on patient survival probabilities. The volcano plot and heat map revealed the differentially expressed genes associated with ADARs in LUAD. Finally, we examined ADAR expression versus immune cell infiltration in LUAD using Spearman's analysis. Using the Gene Expression Profiling Interactive Analysis (GEPIA2) database, we identified the top 100 genes most significantly correlated with ADAR expression, constructed a protein-protein interaction network and performed a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes analysis on these genes. Our results demonstrate that ADARs are overexpressed in LUAD and correlated with poor patient prognosis. ADARs markedly increase the infiltration of T central memory, T helper 2 and T helper cells, while reducing the infiltration of immature dendritic, dendritic and mast cells. Most immune response markers, including T cells, tumor-associated macrophages, T cell exhaustion, mast cells, macrophages, monocytes and dendritic cells, are closely correlated with ADAR expression in LUAD.

[Advances of spatial omics in the individualized diagnosis and treatment of head and neck cancer]

Spatialomics is another research hotspot of biotechnology after single-cell sequencing technology, which can make up for the defect that single-cell sequencing technology can not obtain cell spatial distribution information. Spatialomics mainly studies the relative position of cells in tissue samples to reveal the effect of cell spatial distribution on diseases. In recent years, spatialomics has made new progress in the pathogenesis, target exploration, drug development and many other aspects of head and neck tumors. This paper summarizes the latest progress of spatialomics in the diagnosis and treatment of head and neck cancer.

Impact of tumor suppressor genes inactivation on the multidrug resistance phenotype of hepatocellular carcinoma cells

The response of advanced hepatocellular carcinoma (HCC) to pharmacological treatments is unsatisfactory and heterogeneous. Inactivation of tumor suppressor genes (TSGs) by genetic and epigenetic events is frequent in HCC. This study aimed at investigating the impact of frequently altered TSGs on HCC chemoresistance. TSG alterations were screened by in silico analysis of TCGA-LIHC database, and their relationship with survival was investigated. These TSGs were silenced in HCC-derived cell lines using CRISPR/Cas9. TLDA was used to determine the expression of a panel of 94 genes involved in the resistome. Drug sensitivity, cell proliferation, colony formation and cell migration were assessed. The in silico study revealed the down-regulation of frequently inactivated TSGs in HCC (ARID1A, PTEN, CDH1, and the target of p53, CDKN1A). The presence of TP53 and ARID1A variants and the low expression of PTEN and CDH1 correlated with a worse prognosis of HCC patients. In PLC/PRF/5 cells, ARID1A knockout (ARID1AKO) induced increased sensitivity to cisplatin, doxorubicin, and cabozantinib, without affecting other characteristics of malignancy. PTENKO and E-CadKO showed minimal changes in malignancy, resistome, and drug response. In p53KO HepG2 cells, enhanced malignant properties and higher resistance to cisplatin, doxorubicin, sorafenib, and regorafenib were found. This was associated with changes in the resistome. In conclusion, the altered expression and function of several TSGs are involved in the heterogeneity of HCC chemoresistance and other features of malignancy, contributing to the poor prognosis of these patients. Individual identification of pharmacological vulnerabilities is required to select the most appropriate treatment for each patient.

SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment

Spatial cellular heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx SMI, MERSCOPE, and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels, and transcriptomics coverage. Further application of SpaRx to the state-of-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance, and identifies personalized drug targets and effective drug combinations.

Key Points

We have developed a novel graph-based domain adaption model named SpaRx, to reveal the heterogeneity of spatial cellular response to different types of drugs, which bridges the gap between pharmacogenomics knowledgebase and single-cell spatial transcriptomics data.SpaRx is developed tailored for single-cell spatial transcriptomics data and is provided available as a ready-to-use open-source software, which demonstrates high accuracy and robust performance.SpaRx uncovers that tumor cells located in different areas within tumor lesion exhibit varying levels of sensitivity or resistance to drugs. Moreover, SpaRx reveals that tumor cells interact with themselves and the surrounding microenvironment to form an ecosystem capable of drug resistance.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

Chimerism Testing by Next Generation Sequencing for Detection of Engraftment and Early Disease Relapse in Allogeneic Hematopoietic Cell Transplantation and an Overview of NGS Chimerism Studies

Chimerism monitoring after allogenic Hematopoietic Cell Transplantation (allo-HCT) is critical to determine how well donor cells have engrafted and to detect relapse for early therapeutic intervention. The aim of this study was to establish and detect mixed chimerism and minimal residual disease using Next Generation Sequencing (NGS) testing for the evaluation of engraftment and the detection of early relapse after allo-HCT. Our secondary aim was to compare the data with the existing laboratory method based on Short Tandem Repeat (STR) analysis. One hundred and seventy-four DNA specimens from 46 individuals were assessed using a commercially available kit for NGS, AlloSeq HCT NGS (CareDx), and the STR-PCR assay. The sensitivity, precision, and quantitative accuracy of the assay were determined using artificially created chimeric constructs. The accuracy and linearity of the assays were evaluated in 46 post-transplant HCT samples consisting of 28 levels of mixed chimerism, which ranged from 0.3-99.7%. There was a 100% correlation between NGS and STR-PCR chimerism methods. In addition, 100% accuracy was attained for the two external proficiency testing surveys (ASHI EMO). The limit of detection or sensitivity of the NGS assay in artificially made chimerism mixtures was 0.3%. We conducted a review of all NGS chimerism studies published online, including ours, and concluded that NGS-based chimerism analysis using the AlloSeq HCT assay is a sensitive and accurate method for donor-recipient chimerism quantification and minimal residual disease relapse detection in patients after allo-HCT compared to STR-PCR assay.

Toll-like Receptor 4 Inflammatory Perspective on Doxorubicin-Induced Cardiotoxicity

Doxorubicin (Dox) is one of the most frequently used chemotherapeutic drugs in a variety of cancers, but Dox-induced cardiotoxicity diminishes its therapeutic efficacy. The underlying mechanisms of Dox-induced cardiotoxicity are still not fully understood. More significantly, there are no established therapeutic guidelines for Dox-induced cardiotoxicity. To date, Dox-induced cardiac inflammation is widely considered as one of the underlying mechanisms involved in Dox-induced cardiotoxicity. The Toll-like receptor 4 (TLR4) signaling pathway plays a key role in Dox-induced cardiac inflammation, and growing evidence reports that TLR4-induced cardiac inflammation is strongly linked to Dox-induced cardiotoxicity. In this review, we outline and address all the available evidence demonstrating the involvement of the TLR4 signaling pathway in different models of Dox-induced cardiotoxicity. This review also discusses the effect of the TLR4 signaling pathway on Dox-induced cardiotoxicity. Understanding the role of the TLR4 signaling pathway in Dox-induced cardiac inflammation might be beneficial for developing a potential therapeutic strategy for Dox-induced cardiotoxicity.

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Development and validation of two redox-related genes associated with prognosis and immune microenvironment in endometrial carcinoma

In recent studies, the tumourigenesis and development of endometrial carcinoma (EC) have been correlated significantly with redox. We aimed to develop and validate a redox-related prognostic model of patients with EC to predict the prognosis and the efficacy of immunotherapy. We downloaded gene expression profiles and clinical information of patients with EC from the Cancer Genome Atlas (TCGA) and the Gene Ontology (GO) dataset. We identified two key differentially expressed redox genes (CYBA and SMPD3) by univariate Cox regression and utilised them to calculate the risk score of all samples. Based on the median of risk scores, we composed low-and high-risk groups and performed correlation analysis with immune cell infiltration and immune checkpoints. Finally, we constructed a nomogram of the prognostic model based on clinical factors and the risk score. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. CYBA and SMPD3 were significantly related to the prognosis of patients with EC and used to construct a risk model. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low-and high-risk groups. The nomogram developed with clinical indicators and the risk scores was effective in predicting the prognosis of patients with EC. In this study, a prognostic model constructed based on two redox-related genes (CYBA and SMPD3) were proved to be independent prognostic factors of EC and associated with tumour immune microenvironment. The redox signature genes have the potential to predict the prognosis and the immunotherapy efficacy of patients with EC.

Prognostic value of nectin-4 in human cancers: A meta-analysis

Background

Many reports have described that abnormal nectin-4 expression may be used as a prognostic marker in many tumors. However, these studies failed to reach a consensus. Here, we performed a meta-analysis to comprehensively evaluate the prognostic value of nectin-4 in cancers.

Methods

Relevant studies were identified through a comprehensive search of PubMed, EMBASE and Web of science until August 31, 2022. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to evaluate the relationship between nectin-4 expression and overall survival (OS) and disease-free survival/progression-free survival/relapse-free survival (DFS/PFS/RFS). Odds ratios (ORs) with 95% CIs were applied to assess the relationship between nectin-4 expression and clinicopathologic features. Subgroup analysis was performed to explore the sources of heterogeneity. Sensitivity analysis and funnel plot were used to test the reliability of the results. All data analyses were performed using STATA version 12.0 software.

Results

Fifteen articles involving 2245 patients were included in the meta-analysis. The pooled analysis showed that high nectin-4 expression was significantly associated with poor OS (HR: 1.75, 95% CI: 1.35-2.28). There was no relationship between high nectin-4 expression and DFS/PFS/RFS (HR: 178, 95% CI: 0.78-4.08).Subgroup analyses revealed that that high nectin-4 expression mainly presented adverse OS in esophageal cancer (EC) (HR: 1.78, 95% CI: 1.30-2.44) and gastric cancer (GC) (HR: 1.92, 95% CI: 1.43-2.58). We also found that high nectin-4 expression was associated with tumor diameter (big vs small) (OR: 1.96, 95% CI: 1.02-3.75), tumor stage (III-IV vs I-II) (OR: 2.04, 95% CI: 1.01-4.12) and invasion depth (T3+T4 vs T2+T1) (OR: 3.95, 95% CI: 2.06-7.57).

Conclusions

Nectin-4 can be used as an effective prognostic indicator for specific cancers.

Characterization of prognostic value and immunological roles of RAB22A in hepatocellular carcinoma

Background

The protein-coding gene RAB22A, a member of the RAS oncogene family, is amplified or overexpressed in certain cancers. However, its action mechanism in hepatocellular carcinoma (HCC) remains unclear. Here, we aimed to examine the connection between RAB22A and survival prognosis in HCC and explore the biological significance of RAB22A.

Methods

A database-based pan-cancer expression analysis of RAB22A was performed. Kaplan-Meier analysis and Cox regression were performed to evaluate the association between RAB22A expression and survival prognosis in HCC. Using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), various potential biological functions and regulatory pathways of RAB22A in HCC were discovered. Tumor immune infiltration was studied using the single sample gene set enrichment analysis (ssGSEA) method. N6-methyladenosine modifications and the regulatory network of competitive endogenous RNA (ceRNA) were verified in the TCGA cohort.

Results

RAB22A was upregulated in HCC samples and cell lines. A high RAB22A expression in HCC was strongly correlated with sex, race, age, weight, TNM stage, pathological stage, tumor status, histologic grade, TP53 mutation status, and alpha fetal protein (AFP) levels. Overexpression of RAB22A indicated a poor prognosis was related to overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). GO and KEGG analyses revealed that the differentially expressed genes related to RAB22A might be involved in the proteasomal protein catabolic process, ncRNA processing, ribosome ribosomal subunit, protein serine/threonine kinase activity, protein serine kinase activity, Endocytosis, and non-alcoholic fatty liver disease. GSEA analyses revealed that the differentially expressed genes related to RAB22A might be involved in the T cell receptor, a co-translational protein, that binds to the membrane, axon guidance, ribosome, phagocytosis, and Eukaryotic translation initiation. RAB22A was correlated with N6-methyladenosine expression in HCC and established RAB22A-related ceRNA regulatory networks. Finally,RAB22A expression was positively connected the levels of infiltrating with T helper cells, Tcm cells, and Th2 cells,In contrast, we observed negatively correlations with cytotoxic cells, DCs, and pDCs cells.Moreover,RAB22A expression showed a strong correlation with various immunomarkergroups in HCC.

Conclusions

RAB22A is a potential therapeutic target for improving HCC prognosis and is closely related to immune cell infiltration.