Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

Cellular anatomy of the mouse primary motor cortex.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

Tracing weak neuron fibers.

MOTIVATION: Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons. RESULTS: We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types. AVAILABILITY AND IMPLEMENTATION: Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EZH2 cooperates with gain-of-function p53 mutants to promote cancer growth and metastasis.

In light of the increasing number of identified cancer-driven gain-of-function (GOF) mutants of p53, it is important to define a common mechanism to systematically target several mutants, rather than developing strategies tailored to inhibit each mutant individually. Here, using RNA immunoprecipitation-sequencing (RIP-seq), we identified the Polycomb-group histone methyltransferase EZH2 as a p53 mRNA-binding protein. EZH2 bound to an internal ribosome entry site (IRES) in the 5'UTR of p53 mRNA and enhanced p53 protein translation in a methyltransferase-independent manner. EZH2 augmented p53 GOF mutant-mediated cancer growth and metastasis by increasing protein levels of mutant p53. EZH2 overexpression was associated with worsened outcome selectively in patients with p53-mutated cancer. Depletion of EZH2 by antisense oligonucleotides inhibited p53 GOF mutant-mediated cancer growth. Our findings reveal a non-methyltransferase function of EZH2 that controls protein translation of p53 GOF mutants, inhibition of which causes synthetic lethality in cancer cells expressing p53 GOF mutants.

Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons.

MOTIVATION: To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast (SBC) and the large dynamic range of signal and background both within and across images. RESULTS: We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased five times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience by providing better 3D morphological reconstruction and lower cost of data storage and transfer. AVAILABILITY AND IMPLEMENTATION: The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A deep generative model of 3D single-cell organization.

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional ß-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

CellProfiler 3.0: Next-generation image processing for biology.

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

Vimentin fibers orient traction stress.

The intermediate filament vimentin is required for cells to transition from the epithelial state to the mesenchymal state and migrate as single cells; however, little is known about the specific role of vimentin in the regulation of mesenchymal migration. Vimentin is known to have a significantly greater ability to resist stress without breaking in vitro compared with actin or microtubules, and also to increase cell elasticity in vivo. Therefore, we hypothesized that the presence of vimentin could support the anisotropic mechanical strain of single-cell migration. To study this, we fluorescently labeled vimentin with an mEmerald tag using TALEN genome editing. We observed vimentin architecture in migrating human foreskin fibroblasts and found that network organization varied from long, linear bundles, or "fibers," to shorter fragments with a mesh-like organization. We developed image analysis tools employing steerable filtering and iterative graph matching to characterize the fibers embedded in the surrounding mesh. Vimentin fibers were aligned with fibroblast branching and migration direction. The presence of the vimentin network was correlated with 10-fold slower local actin retrograde flow rates, as well as spatial homogenization of actin-based forces transmitted to the substrate. Vimentin fibers coaligned with and were required for the anisotropic orientation of traction stresses. These results indicate that the vimentin network acts as a load-bearing superstructure capable of integrating and reorienting actin-based forces. We propose that vimentin's role in cell motility is to govern the alignment of traction stresses that permit single-cell migration.

Wilms' tumor 1-associating protein plays an aggressive role in diffuse large B-cell lymphoma and forms a complex with BCL6 via Hsp90.

BACKGROUND: Wilms' tumor 1-associating protein (WTAP) is a nuclear protein, which is ubiquitously expressed in many tissues. Furthermore, in various types of malignancies WTAP is overexpressed and plays a role as an oncogene. The function of WTAP in diffuse large B-cell lymphoma (DLBCL), however, remains unclear. METHODS: Immunohistochemistry was applied to evaluate the levels of WTAP expression in DLBCL tissues and normal lymphoid tissues. Overexpression and knock-down of WTAP in DLBCL cell lines, verified on mRNA and protein level served to analyze cell proliferation and apoptosis in DLBCL cell lines by flow cytometry. Finally, co-immunoprecipitation (Co-IP), IP, and GST-pull down assessed the interaction of WTAP with Heat shock protein 90 (Hsp90) and B-cell lymphoma 6 (BCL6) as well as determined the extend of its ubiquitinylation. RESULTS: WTAP protein levels were consistently upregulated in DLBCL tissues. WTAP promoted DLBCL cell proliferation and improved the ability to confront apoptosis, while knockdown of WTAP in DLBCL cell lines allowed a significant higher apoptosis rate after treatment with Etoposide, an anti-tumor drug. The stable expression of WTAP was depended on Hsp90. In line, we demonstrated that WTAP could form a complex with BCL6 via Hsp90 in vivo and in vitro. CONCLUSION: WTAP is highly expressed in DLBCL, promoting growth and anti-apoptosis in DLBCL cell lines. WTAP is a client protein of Hsp90 and can appear in a complex with BCL6 and Hsp90 in DLBCL. Down-regulation of WTAP could improve the chemotherapeutic treatments in DLBCL.

BRCA1 is a negative modulator of the PRC2 complex.

The Polycomb-repressive complex 2 (PRC2) is important for maintenance of stem cell pluripotency and suppression of cell differentiation by promoting histone H3 lysine 27 trimethylation (H3K27me3) and transcriptional repression of differentiation genes. Here we show that the tumour-suppressor protein BRCA1 interacts with the Polycomb protein EZH2 in mouse embryonic stem (ES) and human breast cancer cells. The BRCA1-binding region in EZH2 overlaps with the noncoding RNA (ncRNA)-binding domain, and BRCA1 expression inhibits the binding of EZH2 to the HOTAIR ncRNA. Decreased expression of BRCA1 causes genome-wide EZH2 re-targeting and elevates H3K27me3 levels at PRC2 target loci in both mouse ES and human breast cancer cells. BRCA1 deficiency blocks ES cell differentiation and enhances breast cancer migration and invasion in an EZH2-dependent manner. These results reveal that BRCA1 is a key negative modulator of PRC2 and that loss of BRCA1 inhibits ES cell differentiation and enhances an aggressive breast cancer phenotype by affecting PRC2 function.

Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy.

Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in neuroimaging for its ability to capture high-resolution 3D neural data. However, the presence of stripe noise significantly degrades image quality, particularly in complex 3D stripes with varying widths and brightness, posing challenges in neuroscience research. Existing stripe removal algorithms excel in suppressing noise and preserving details in 2D images with simple stripes but struggle with the complexity of 3D stripes. To address this, we propose a novel 3D U-net model for Stripe Removal in Light sheet fluorescence microscopy (USRL). This approach directly learns and removes stripes in 3D space across different scales, employing a dual-resolution strategy to effectively handle stripes of varying complexities. Additionally, we integrate a nonlinear mapping technique to normalize high dynamic range and unevenly distributed data before applying the stripe removal algorithm. We validate our method on diverse datasets, demonstrating substantial improvements in peak signal-to-noise ratio (PSNR) compared to existing algorithms. Moreover, our algorithm exhibits robust performance when applied to real LSFM data. Through extensive validation experiments, both on test sets and real-world data, our approach outperforms traditional methods, affirming its effectiveness in enhancing image quality. Furthermore, the adaptability of our algorithm extends beyond LSFM applications to encompass other imaging modalities. This versatility underscores its potential to enhance image usability across various research disciplines.

Ichthyosis uteri complicated by endometrial adenocarcinoma with transitional cell differentiation: A case report.

RATIONALE: Ichthyosis uteri is a rare pathological condition characterized by the replacement of the endometrial lining by stratified squamous epithelium. Yet its occurrence with endometrial adenocarcinoma is very rare. PATIENT CONCERNS: A 68-year-old woman has been experiencing sporadic, minor vaginal hemorrhages for a few months. The gynecological evaluation revealed a uterine enlargement and imaging demonstrated an irregular mass within the uterus. DIAGNOSIS: Endometrial adenocarcinoma with transitional cell differentiation; ichthyosis uteri with dysplasia. INTERVENTIONS: Radical hysterectomy with pelvic lymphadenectomy was performed followed by postoperative radiotherapy. OUTCOMES: Postoperative follow-up at 8 months showed a favorable outcome without signs of recurrence and metastasis. LESSONS: Adequate pathological sampling is crucial to identifying the accompanying lesions of ichthyosis uteri. Finding molecular alterations in various pathological morphologies is important to understand the evolution of disease.

A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China.

BACKGROUND: In China, rapid intraoperative diagnosis of frozen sections of thyroid nodules is used to guide surgery. However, the lack of subspecialty pathologists and delayed diagnoses are challenges in clinical treatment. This study aimed to develop novel diagnostic approaches to increase diagnostic effectiveness. METHODS: Artificial intelligence and machine learning techniques were used to automatically diagnose histopathological slides. AI-based models were trained with annotations and selected as efficientnetV2-b0 from multi-set experiments. RESULTS: On 191 test slides, the proposed method predicted benign and malignant categories with a sensitivity of 72.65%, specificity of 100.0%, and AUC of 86.32%. For the subtype diagnosis, the best AUC was 99.46% for medullary thyroid cancer with an average of 237.6 s per slide. CONCLUSIONS: Within our testing dataset, the proposed method accurately diagnosed the thyroid nodules during surgery.

NRRS: a re-tracing strategy to refine neuron reconstruction.

It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction. Availability and implementation: The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline.

Background: Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons. Methods: For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting. Results: Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions.

Cervical squamous intraepithelial lesions (SILs) are precursor lesions of cervical cancer, and their accurate diagnosis enables patients to be treated before malignancy manifests. However, the identification of SILs is usually laborious and has low diagnostic consistency due to the high similarity of pathological SIL images. Although artificial intelligence (AI), especially deep learning algorithms, has drawn a lot of attention for its good performance in cervical cytology tasks, the use of AI for cervical histology is still in its early stages. The feature extraction, representation capabilities, and use of p16 immunohistochemistry (IHC) among existing models are inadequate. Therefore, in this study, we first designed a squamous epithelium segmentation algorithm and assigned the corresponding labels. Second, p16-positive area of IHC slides were extracted with Whole Image Net (WI-Net), followed by mapping the p16-positive area back to the H&E slides and generating a p16-positive mask for training. Finally, the p16-positive areas were inputted into Swin-B and ResNet-50 to classify the SILs. The dataset comprised 6171 patches from 111 patients; patches from 80% of the 90 patients were used for the training set. The accuracy of the Swin-B method for high-grade squamous intraepithelial lesion (HSIL) that we propose was 0.914 [0.889-0.928]. The ResNet-50 model for HSIL achieved an area under the receiver operating characteristic curve (AUC) of 0.935 [0.921-0.946] at the patch level, and the accuracy, sensitivity, and specificity were 0.845, 0.922, and 0.829, respectively. Therefore, our model can accurately identify HSIL, assisting the pathologist in solving actual diagnostic issues and even directing the follow-up treatment of patients.

High-throughput confocal airy beam oblique light-sheet tomography of brain-wide imaging at single-cell resolution.

Brain research is an area of research characterized by its cutting-edge nature, with brain mapping constituting a crucial aspect of this field. As sequencing tools have played a crucial role in gene sequencing, brain mapping largely depends on automated, high-throughput and high-resolution imaging techniques. Over the years, the demand for high-throughput imaging has scaled exponentially with the rapid development of microscopic brain mapping. In this paper, we introduce the novel concept of confocal Airy beam into oblique light-sheet tomography named CAB-OLST. We demonstrate that this technique enables the high throughput of brain-wide imaging of long-distance axon projection for the entire mouse brain at a resolution of 0.26 µm × 0.26 µm × 1.06 µm in 58 hours. This technique represents an innovative contribution to the field of brain research by setting a new standard for high-throughput imaging techniques.

Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy.

Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.

STING agonism overcomes STAT3-mediated immunosuppression and adaptive resistance to PARP inhibition in ovarian cancer.

BACKGROUND: Poly (ADP-ribose) polymerase (PARP) inhibition (PARPi) has demonstrated potent therapeutic efficacy in patients with BRCA-mutant ovarian cancer. However, acquired resistance to PARPi remains a major challenge in the clinic. METHODS: PARPi-resistant ovarian cancer mouse models were generated by long-term treatment of olaparib in syngeneic Brca1-deficient ovarian tumors. Signal transducer and activator of transcription 3 (STAT3)-mediated immunosuppression was investigated in vitro by co-culture experiments and in vivo by analysis of immune cells in the tumor microenvironment (TME) of human and mouse PARPi-resistant tumors. Whole genome transcriptome analysis was performed to assess the antitumor immunomodulatory effect of STING (stimulator of interferon genes) agonists on myeloid cells in the TME of PARPi-resistant ovarian tumors. A STING agonist was used to overcome STAT3-mediated immunosuppression and acquired PARPi resistance in syngeneic and patient-derived xenografts models of ovarian cancer. RESULTS: In this study, we uncover an adaptive resistance mechanism to PARP inhibition mediated by tumor-associated macrophages (TAMs) in the TME. Markedly increased populations of protumor macrophages are found in BRCA-deficient ovarian tumors that rendered resistance to PARPi in both murine models and patients. Mechanistically, PARP inhibition elevates the STAT3 signaling pathway in tumor cells, which in turn promotes protumor polarization of TAMs. STAT3 ablation in tumor cells mitigates polarization of protumor macrophages and increases tumor-infiltrating T cells on PARP inhibition. These findings are corroborated in patient-derived, PARPi-resistant BRCA1-mutant ovarian tumors. Importantly, STING agonists reshape the immunosuppressive TME by reprogramming myeloid cells and overcome the TME-dependent adaptive resistance to PARPi in ovarian cancer. This effect is further enhanced by addition of the programmed cell death protein-1 blockade. CONCLUSIONS: We elucidate an adaptive immunosuppression mechanism rendering resistance to PARPi in BRCA1-mutant ovarian tumors. This is mediated by enrichment of protumor TAMs propelled by PARPi-induced STAT3 activation in tumor cells. We also provide a new strategy to reshape the immunosuppressive TME with STING agonists and overcome PARPi resistance in ovarian cancer.

KIF23 is a potential biomarker of diffuse large B cell lymphoma: Analysis based on bioinformatics and immunohistochemistry.

ABSTRACT: Diffuse Large B Cell Lymphoma (DLBCL), the most common form of blood cancer. The genetic and clinical heterogeneity of DLBCL poses a major barrier to diagnosis and treatment. Hence, we aim to identify potential biomarkers for DLBCL.Differentially expressed genes were screened between DLBCL and the corresponding normal tissues. Kyoto Encyclopedia of Genes and Genomes and Gene oncology analyses were performed to obtain an insight into these differentially expressed genes. PPI network was constructed to identify hub genes. survival analysis was applied to evaluate the prognostic value of those hub genes. DNA methylation analysis was implemented to explore the epigenetic dysregulation of genes in DLBCL.In this study, Kinesin family member 23 (KIF23) showed higher expression in DLBCL and was identified as a risk factor in DLBCL. The immunohistochemistry experiment further confirmed this finding. Subsequently, the univariate and multivariate analysis indicated that KIF23 might be an independent adverse factor in DLBCL. Upregulation of KIF23 might be a risk factor for the overall survival of patients who received an R-CHOP regimen, in late-stage, whatever with or without extranodal sites. Higher expression of KIF23 also significantly reduced 3, 5, 10-year overall survival. Furthermore, functional enrichment analyses (Kyoto Encyclopedia of Genes and Genomes, Gene oncology, and Gene Set Enrichment Analysis) showed that KIF23 was mainly involved in cell cycle, nuclear division, PI3K/AKT/mTOR, TGF-beta, and Wnt/beta-catenin pathway in DLBCL. Finally, results of DNA methylation analysis indicated that hypomethylation in KIF23's promoter region might be the result of its higher expression in DLBCL.The findings of this study suggested that KIF23 is a potential biomarker for the diagnosis and prognosis of DLBCL. However, further studies were needed to validate these findings.