iCell
type: channel
description: iCell is a newly established, peer-reviewed journal dedicated to the dissemination of cutting-edge research in cellular biology. The journal focuses on innovative studies that advance our understanding of cellular processes, mechanisms, and applications in medicine and biotechnology.
language: en
publication name: iCell
items: Artificial Intelligence for Single-Cell Biology: From Representation Learning to Predictive Modeling
items: Dynamic Profiling of the Tumor Microenvironment in Lung Squamous Cell Carcinoma by Multidimensional Transcriptomics
items: Artificial Intelligence in Immunoinformatics: From Multi-Omics to Precision Immunology
items: Crosstalk Between Gingival Mesenchymal Stem Cells and the Inflammatory Microenvironment
items: Detection and genetic characterization of Densvirus from Oriental Pied hornbill in Guangxi, China
items: HiOmics 2.0: An All-in-One Intelligent Cloud Platform for Multi-Omics Analysis in Biomedical Research
items: Investigating the Role of Pangolin Pseudogenes in Host-Pathogen Immune Interactions: A BiLSTM-Based Approach
items: Advancing STEC Bioinformatics Training for the New Era
items: Circular RNA circR-4600 inhibits hepatocellular carcinoma progression by suppressing autophagy through DVL3 and DAB2 interaction
items: Enhancing Undergraduate Academic Writing in the Context of Medical Research: A Comprehensive Training Framework
items: A Compendium of ERVs in the Genomes of Major Primates
items: Revealing Causal Genes in Nasopharyngeal Carcinoma: An Integrated Approach of Mendelian Randomization and Single-Cell Sequencing
items: Construction of an Integrated Intelligent Database Platform for Antimicrobial-Resistant Strains
items: A bibliometric analysis of RNA methylation in prostate cancer and its complications from 2003 to 2024
items: From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models
items: JNK1 downregulation confers fibroblasts with a mammary epithelial cell fate
items: Resveratrol Oligomers Trans-gnetin H Promotes Reactive Oxygen Species Generation and Rictor Degradation to Suppress Cancer Cell Viability
items: Annotation and target analysis of human endogenous retroviruses
items: Exosomal miRNAs in Inflammatory Diseases
items: Revealing PAM as a Prognostic Biomarker and Therapeutic Target in Clear Cell Renal Cell Carcinoma
items: The Mechanism of RNA Virus-mediated Tumorigenesis
items: Long COVID: Current Status, Challenges and Future Directions
items: RNA-Binding Proteins: Biological Mechanisms and Their Impact on Osteoporosis Development
items: Pan-Cancer Analysis for Identification of Tumor Antigens and Immune Subtypes in mRNA Vaccine Development
items: TGDD: A Genomics-Based Database for Tumor Treatment Drugs
items: Prognostic Implications and Immune Infiltration in the Nod-like receptor signaling pathway: A Comprehensive Analysis across Pan-cancer

Artificial Intelligence for Single-Cell Biology: From Representation Learning to Predictive Modeling
type: item
description: While single-cell sequencing technologies provide a high-resolution approach for dissecting cellular heterogeneity, their data are inherently high-dimensional, sparse, noisy, and strongly affected by batch effects and limited annotations. These properties make artificial intelligence (AI), particularly deep generative and probabilistic models, more suitable for analyzing single-cell data. Recent AI frameworks, including variational inference–based model scVI and its extensions, have supported unified pipelines for normalization, representation learning, batch correction, multimodal integration, and downstream analyses. Specialized downstream analyses, such as scalable cell-type annotation, trajectory and dynamic inference, cell–cell communication analysis, spatial mapping, and the prediction of genetic or pharmacological perturbation responses, can be extended by learning transferable latent representations. Emerging self-supervised foundation models promise reusable cellular representations across tasks, tissues, and species. Addressing challenges in benchmarking, interpretability, uncertainty quantification, and robust generalization highlights future frontiers in the development of predictive and causal single-cell models.
subject: Reviews
  topic: section
creator: Wenxing Li
creator: YUXIANG LUO
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/YAGA2665

Dynamic Profiling of the Tumor Microenvironment in Lung Squamous Cell Carcinoma by Multidimensional Transcriptomics
type: item
description: Therapeutic options for lung squamous cell carcinoma (LSCC) remain limited and vary by TNM stage. Achieving precise, stage-dependent treatment requires an understanding of how the tumor microenvironment (TME) changes during disease progression. Therefore, we investigated stage-related TME changes and explored prognostic strategies based on multidimensional transcriptomic data. In this study, we comprehensively characterize the TME during different stages of LSCC progression by integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and bulk RNA sequencing (bulk RNA-seq). The TME of LSCC displayed marked heterogeneity across TNM stages. Notably, the proportion of SPP1⁺ macrophages increased progressively from early to late disease and was associated with heightened immunosuppressive activity. We constructed a prognostic model based on differentially expressed genes in SPP1⁺ macrophages between stage IV and stage I. We found SPP1⁺ macrophages and fibroblasts exhibited the most frequent ligand-receptor interactions at the tumor edge. Our study revealed stage-dependent remodeling of the LSCC TME and highlighted SPP1⁺ macrophage subgroup as key contributors to tumor progression. The SPP1⁺ macrophage–based prognostic model may facilitate risk stratification and inform future therapeutic strategies.
subject: Articles
  topic: section
subject: Lung squamous cell carcinoma; Macrophages; Osteopontin; RNA-Seq; Tumor Microenvironment.
  topic: keywords
creator: Dandan Sun
creator: Zixuan Liu
creator: Jiaqi Gu
creator: Quanlei Wang
creator: Anxin Gu
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/WEQA7681

Artificial Intelligence in Immunoinformatics: From Multi-Omics to Precision Immunology
type: item
description: The immune system is a multilayered, adaptive network whose behavior emerges across molecules, cells, tissues, and time. Contemporary immunology therefore generates high-dimensional, heterogeneous datasets that strain classical analytical assumptions. Artificial intelligence (AI), spanning machine learning and deep learning, is increasingly becoming a core paradigm for extracting structure, prediction, and actionable insight from these data. This review summarizes how AI is transforming key steps of modern immunology, from sequencing- and repertoire-based analysis to antigen specificity and epitope prioritization for vaccine design; from single-cell and spatial profiling to inference of immune states and cell–cell communication; and from multi-omics integration to prediction and optimization of immunotherapy responses, including immune checkpoint blockade and CAR-based therapies. We compare the strengths and limitations of major model families—convolutional and recurrent networks, graph neural networks, generative models, and transformer-based architectures—highlighting how their inductive biases map to immunological questions. We also discuss barriers to broad adoption, including data standardization and metadata quality, interpretability and uncertainty calibration, computational costs, and gaps between benchmark performance and clinical generalizability. Finally, we outline a roadmap toward interpretable and uncertainty-aware models, cross-center data sharing and benchmarking, closed-loop “dry–wet” validation with perturbation experiments, and clinically deployable pipelines for personalized immunodiagnosis and therapy.
subject: Reviews
  topic: section
subject: Artificial Intelligence; Machine Learning; Deep Learning; Bioinformatics; Computational Immunology; Immunomics; Vaccine Design; Immunotherapy; Multi-omics Integration
  topic: keywords
creator: ZiYi Liu
creator: Sanqi An
creator: Wenxing Li
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/SAOV9257

Crosstalk Between Gingival Mesenchymal Stem Cells and the Inflammatory Microenvironment
type: item
description: Gingival mesenchymal stem cells (GMSCs) hold similar biological features to other MSCs, but also have some advantages that include being easily accessible with relatively few technical and ethical constraints, stronger proliferative capacity, and more stable morphological characteristics. A close bidirectional interaction exists between the biological functions of GMSCs and their inflammatory microenvironment: inflammatory factors can regulate the immunomodulatory and tissue repair capabilities of GMSCs, while GMSCs, in turn, can reshape the local microenvironment, suppressing excessive inflammation and promoting tissue regeneration. Herein, we discuss the biological properties and functional alterations of GMSCs under various pathophysiological conditions, aiming to provide new insights into the immunoregulatory mechanisms of GMSCs in tissue regeneration and therapeutic processes.
subject: Reviews
  topic: section
subject: GMSCs; inflammation; immunomodulation
  topic: keywords
creator: Qilin Mai
creator: Song Guo Zheng
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/NGLC9352

Detection and genetic characterization of Densvirus from Oriental Pied hornbill in Guangxi, China
type: item
description: Densoviruses (DNVs), members of the family Parvoviridae, infect a broad range of invertebrate and vertebrate hosts, yet their presence in birds remains poorly understood. Densoviruses have been increasingly recognized for their ecological roles in controlling insect populations and potentially affecting vertebrate hosts through trophic interactions. In this study, we investigated DNVs in the Oriental Pied hornbill, a widespread species in Southeast Asia and Southern China. Thirteen fecal samples were collected and analyzed using viral metagenomics and molecular techniques. PCR screening detected DNVs in two samples, and complete genomes of two strains (GBF3 and GBF4) were successfully obtained. Phylogenetic analysis of conserved NS1 and VP1 regions showed that both strains belong to the genus Densovirus (subfamily Densovirinae) and cluster closely with PmDNV-JL previously identified in Parus major. These findings represent the first genomic evidence of DNVs in Oriental Pied hornbills, emphasizing the potential for cross-species transmission and the importance of birds as ecological reservoirs of arthropod-associated viruses. Our study also provides insights into viral diversity, host adaptation, and the evolutionary dynamics of DNVs in natural ecosystems. Future research should explore the replication capacity and pathogenic potential of DNVs in avian hosts to assess their epidemiological significance.
subject: Articles
  topic: section
subject: Oriental Pied hornbill,
  topic: keywords
subject: densoviruses (DNVs)
  topic: keywords
subject: viral metagenomics
  topic: keywords
subject: complete genome
  topic: keywords
subject: phylogenetic analysis
  topic: keywords
creator: NADA HASSAN
creator: Lanxiang Li
creator: Mariusz Giergiel
creator: Teng- Cheng Que
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/SMVL4050

HiOmics 2.0: An All-in-One Intelligent Cloud Platform for Multi-Omics Analysis in Biomedical Research
type: item
description: Rapid advances in high-throughput sequencing and mass spectrometry have introduced substantial challenges for multi-omics data analysis, including fragmented workflows, slow interactive responsiveness, and limited reproducibility of results. To address these issues, we present HiOmics 2.0 (https://henbio.com), a substantially upgraded cloud platform. The core innovation is a hierarchical task-scheduling system that decouples lightweight interactive tasks from compute-intensive analytical pipelines and enables real-time responses via a dedicated API cluster. This redesign reduces the generation time of common analytical plots from over 30 seconds to below 5 seconds (an improvement exceeding 80%), delivering responsiveness comparable to local desktop software. Building on this foundation, the platform adds modules for single-cell transcriptomics, proteomics, metabolomics, pathogen detection, and Mendelian randomization. Accordingly, HiOmics has evolved from a collection of discrete tools into an integrated infrastructure that supports systematic life science research. The unified platform enables users to complete an end-to-end research workflow—from data preprocessing and multi-omics mining to model development and visualization. As of November 2025, HiOmics 2.0 integrates 453 analytical tools, has recorded 112,957 visits from users worldwide, and has completed 36,726 analytical tasks. Representative case studies demonstrate its utility in both basic research and clinical translation. Overall, through architectural innovation and functional integration, HiOmics 2.0 provides an efficient, user-friendly, and reproducible all-in-one solution for life science research.
subject: Articles
  topic: section
creator: Zhining Zhang
creator: Yuanyuan Tang
creator: Yunlin He
creator: Yong Xu
creator: Zhiwei Lu
creator: Kangming He
creator: Hong Qiu
creator: Yongjian Huang
creator: Juan Huang
creator: Honghong Pu
creator: Yanling Hu
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/PILY3289

Investigating the Role of Pangolin Pseudogenes in Host-Pathogen Immune Interactions: A BiLSTM-Based Approach
type: item
description: This study integrates a Bidirectional Long Short-Term Memory (BiLSTM) deep learning framework with bioinformatics approaches to elucidate the functional role of pangolin pseudogenes in the immune interactions between tick-borne pathogens and their hosts. We developed a novel BiLSTM-autoencoder model incorporating dynamic weight positional encoding and a multi-head self-attention mechanism, effectively capturing pseudogene sequence features while overcoming limitations inherent in traditional analytical methods. Genome-wide screening of the Manis pentadactyla (GCF_030020395.1) and Manis javanica (GCF_001685135.1) assemblies identified 3,209 and 2,035 pseudogenes, respectively. Subsequent filtration yielded 94 immune-related homologous genes, classified into ten distinct immune system pathways. Our analysis reveals significant species-specific variation and functional plasticity in pangolin pseudogenes: Chinese Pangolin LILRA6 Pseudogenes: Fourteen identified variants modulate bacterial recognition and inflammatory responses through specific amino acid deletions. Interferon Receptor Pseudogenes (e.g., XP_036870501.1): Critical mutations disrupt JAK-STAT signaling pathway functionality. Toll-like Receptor (TLR) Pseudogenes: Reconstruction error values (ranging from 28.149 to 68.957) correlate strongly with structural integrity. Notably, the disrupted LRR domain in the giant pangolin sequence KAK2502118.1 (error = 65.986) suggests an adaptive immune strategy. Furthermore, we identified a potential molecular interaction between a pangolin-derived 52 kDa sequence and the 8.9 kDa salivary protease of Amblyomma javanense, providing novel insights into tick-borne transmission mechanisms. This study represents the first application of deep learning to elucidate the functional role of pangolin pseudogenes, confirming their active involvement in immune resistance. It establishes a new paradigm for investigating host-pathogen interactions and provides a critical foundation for t…
subject: Articles
  topic: section
subject: Pathogen; Pangolin; Immune interaction; Pseudogene
  topic: keywords
creator: Tengcheng Que
creator: Zhining Zhang
creator: Yunlin He
creator: Qiuyu Wu
creator: Jinying He
creator: Xinni Yang
creator: Panyu Chen
creator: Hong Qiu
creator: Yuankun Liu
creator: Hua Zhang
creator: Wenjian Liu
rights: Copyright (c) 2025 iCell
date: 2025-11-28
publication date: 2025-11-28
doi: 10.71373/PZDB5938

Advancing STEC Bioinformatics Training for the New Era
type: item
description: Bioinformatics, positioned at the nexus of life, data, and computational sciences, is pivotal for cultivating innovators and enabling technology translation. Its growing importance, driven by the quantitative shift in life sciences, is met with universal challenges: outdated curricula, siloed pedagogy, and inadequate lifelong learning frameworks. To address these, the Guangdong-Guangxi-Hainan Bioinformatics Education Consortium convened in Nanning, China (14–16 November 2025). Guided by the STEC principle (Science, Technology, Engineering, Collaboration), educators and industry experts presented practical cases aligned with global educational trends. This framework offers a concrete strategy to tackle seven core challenges in contemporary bioinformatics education, providing a roadmap for its sustainable advancement.
subject: Editorial
  topic: section
subject: bioinformatics
  topic: keywords
subject: STEC
  topic: keywords
subject: Educational innovation
  topic: keywords
subject: Inter-regional synergy
  topic: keywords
subject: AI-augmented pedagogy
  topic: keywords
subject: Lifelong learning ecosystems
  topic: keywords
creator: Ming Chen
creator: Hao Liang
creator: Yanling Hu
creator: Dijun Chen
creator: Hong Li
creator: Zunnan Huang
creator: Li Ye
creator: Jian Ren
creator: Haixia Long
creator: Chengjian Jiang
creator: Guangfu Pang
creator: Bihui Huang
creator: Zexian Liu
creator: Sanqiang Gong
creator: Sanqi An
creator: Xiaomin Wang
creator: Shunhan Yao
creator: Jianlong Li
creator: Tianpeng Wang
creator: Xiaoying Zhou
creator: Bo Liu
creator: Jiemei Chu
creator: Pei Wang
creator: Lili Xu
creator: Tianci Wang
creator: Mengze Nie
creator: Rongquan He
creator: Gang Chen
creator: Dan-Dan Xiong
creator: Guoying Li
creator: Dan Wang
creator: Jiandi Li
creator: Kai Huang
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/AUEH9795

Circular RNA circR-4600 inhibits hepatocellular carcinoma progression by suppressing autophagy through DVL3 and DAB2 interaction
type: item
description: Circular RNA (circRNA) plays a crucial role in regulating autophagy in hepatocellular carcinoma (HCC); however, the specific mechanisms underlying this regulation remain poorly understood. This study investigates the role and mechanisms of circR-4600 in HCC, aiming to identify novel therapeutic targets to enhance patient prognosis. Differential expression of circRNA in HCC and adjacent non-tumor tissues from six patients was assessed using circRNA microarray analysis. RNA pulldown combined with ESI-FT-ICR-MS identified DVL3 as the RNA-binding protein that interacts with circR-4600. Functional assays, including overexpression and knockdown experiments in HCC cell lines, as well as in vivo studies, were conducted to evaluate the effects.The results demonstrated significantly lower circR-4600 expression in HCC tissues, which negatively correlated with poor prognosis. Overexpression of circR-4600 inhibited HCC cell proliferation, migration, and invasion in vitro, and reduced lung metastasis in vivo. Mechanistically, circR-4600 interacted with DVL3 to suppress ATG5-mediated autophagy, thereby diminishing DVL3’s inhibitory effect on DAB2, which led to a decreased LC3 II/I ratio and epithelial-mesenchymal transition, while increasing P62 accumulation. In conclusion, circR-4600 modulates autophagy through DVL3 and DAB2, thereby inhibiting HCC progression and providing insights into its potential as a therapeutic target.
subject: Articles
  topic: section
subject: CircRNA
  topic: keywords
subject: Hepatocellular carcinoma
  topic: keywords
subject: Autophagy
  topic: keywords
subject: DVL3
  topic: keywords
subject: DAB2
  topic: keywords
creator: Gang Wang
creator: Shaoliang Zhu
creator: Zhizhong Chen
creator: Yulin Yuan
creator: Qiuxia Wei
creator: Hongwei Wang
creator: Tao Yang
creator: Ling Cai
creator: Xiaosu Zou
creator: Wenqian Nong
creator: Qicong Chen
creator: Lequn Li
creator: Xiaofeng Dong
creator: Feixiang Wu
creator: Honglin Luo
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/XEDR3565

Enhancing Undergraduate Academic Writing in the Context of Medical Research: A Comprehensive Training Framework
type: item
description: Objective: Scientific writing is essential for students to engage in research and contribute meaningfully to academic communities. Yet many undergraduates lack formal training in how to write clearly and rigorously. This often leads to work that falls short in logic, structure, and originality. This study asks how a structured, multi-component training framework can improve academic writing skills and research output among undergraduates. Methods: We present a long-term case study from Guangdong Medical University, where an undergraduate research group in computer-aided drug design implemented a comprehensive academic writing program. The framework combines four strategies: a student-led writing platform, peer mentorship and collaborative learning, regular outcome-focused seminars, and bilingual, practice-driven writing instruction. Results: Between 2015 and 2024, over 300 students participated. Many produced publishable manuscripts, with 30 Science Citation Index papers authored by undergraduates. These outcomes reflect both skill development and sustained research engagement. Conclusion: Our work offers a tested model for building academic writing capacity at scale. It may serve as a guide for institutions seeking to equip students especially non-native English speakers with the skills to write and publish scientific work.
subject: Editorial
  topic: section
subject: Undergraduates
  topic: keywords
subject: Innovative scientific research
  topic: keywords
subject: Talent development
  topic: keywords
subject: Scientific article writing
  topic: keywords
subject: Training framework
  topic: keywords
creator: Yong Cao
creator: Ying Wen
creator: Xuan Li
creator: Hongbin Huang
creator: Liyi Zou
creator: Yong Liu
creator: Zunna Huang
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/MKIN4148

A Compendium of ERVs in the Genomes of Major Primates
type: item
description: ERVs integrate into host DNA, forming vertically inherited proviral sequences. These sequences not only record the historical imprints of ancient retroviruses but also play significant biological roles in modern host genomes. The widespread distribution of ERVs in primate genomes suggests that constructing a unified system for ERV site distribution would facilitate the study of their impact on host genomic biology and evolution. In this study, we utilized LTR_harvest to predict candidate ERV sequences in primate genomes and further identified and annotated the characteristics of these candidate sequences using LTR_digest software. We have compiled a comprehensive and well-annotated database (http://ervsdb.omics.henbio.com/home/index/index.html?idx=1). This database enables the acquisition of ERV data and corresponding neighboring genes for 61 primate species, characterization of ERV proviral structures, assessment of integration sites, and prediction of potential regulatory gene functions associated with ERV-LTRs. Using the Trachypithecus francoisi as an example, we have preliminarily explored the potential influence of ERVs on host resistance or susceptibility to diseases, providing a theoretical reference for further research on ERVs in other animals.
subject: Articles
  topic: section
subject: Primates
  topic: keywords
subject: ERVs
  topic: keywords
subject: Trachypithecus francoisi
  topic: keywords
creator: Yifeng Li
creator: Chunlan Jiang
creator: Lanxiang Li
creator: Zhining Zhang
creator: Yunlin He
creator: Kangming He
creator: Zhiwei Lu
creator: Yongjian Huang
creator: Boying Liang
rights: Copyright (c) 2025 iCell
date: 2025-12-10
publication date: 2025-12-10
doi: 10.71373/SBHJ1439

Revealing Causal Genes in Nasopharyngeal Carcinoma: An Integrated Approach of Mendelian Randomization and Single-Cell Sequencing
type: item
description: Objective: This study employed Mendelian randomization (MR) to identify genes associated with nasopharyngeal carcinoma (NPC) and utilized single-cell sequencing to analyze their expression patterns within the tumor microenvironment. Methods: Transcriptomic datasets (GSE53819 and GSE12452) were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed using R software to identify common differentially expressed genes. Summary-level data for expression quantitative trait loci (eQTLs) of these genes and for NPC genome-wide association studies (GWAS) were sourced from the OpenGWAS and FinnGen databases, respectively. Using gene-associated single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), MR analyses—including the Wald ratio, inverse variance weighted (IVW), MR-Egger regression, and weighted median methods—were conducted to evaluate the causal effects of these genes on NPC, adhering to the three core assumptions of MR. Additionally, single-cell RNA sequencing datasets (GSE150825 and GSE120926) from GEO were processed for quality control, cell clustering, and annotation. The expression of MR-identified genes across cell subtypes was visualized using DotPlot. Results: Differential expression analysis identified 494 common differentially expressed genes, of which 313 were available for MR analysis. The MR results indicated that MFSD4 (Wald ratio) as well as PSPH and VPREB3 (IVW) were associated with an increased risk of NPC, whereas TMEM200A and THBS2 (IVW) were associated with a reduced risk. Single-cell expression analysis revealed that PSPH was predominantly expressed in epithelial cells, TMEM200A was highly expressed in fibroblasts and T cells, THBS2 was mainly expressed in fibroblasts, MFSD4 showed higher expression in endothelial cells, and VPREB3 was broadly highly expressed in tumor cells, plasma B cells, and B cells—with the highest expression observed in B cells. Conclusion: By integrating Mendel…
subject: Articles
  topic: section
subject: nasopharyngeal carcinoma
  topic: keywords
subject: differentially expressed genes
  topic: keywords
subject: mendelian randomization
  topic: keywords
subject: single-cell sequencing
  topic: keywords
creator: Yuxiao He
creator: Bin Han
creator: Xuanyu Pan
creator: Jianping Zhou
creator: Tingwei Lv
creator: Xingrao Li
creator: Yongjian Zhou
creator: Yani Yang
creator: Tingting Liu
creator: Yanling Hu
rights: Copyright (c) 2025 iCell
date: 2025-12-25
publication date: 2025-12-25
doi: 10.71373/KKRB6502

Construction of an Integrated Intelligent Database Platform for Antimicrobial-Resistant Strains
type: item
description: Abstract:Antimicrobial resistance (AMR) poses a severe global public health threat, causing over 700,000 deaths annually. The fragmentation of AMR data hinders comprehensive research, clinical decision-making, and public health surveillance. Existing databases like CARD and CARSS often suffer from data silos, limited analytical tools, and insufficient localized data. This study aims to construct an integrated, operable database for drug-resistant bacterial strains to support AMR research and control.Methods:The study involved: (1) Systematic collection of data on resistant bacteria, genomes, and antimicrobial substances from public databases and literature; (2) Design of a relational database structure following normalization paradigms (1NF, 2NF, 3NF) and implementation using MySQL; (3) Data processing and batch import via Python and pandas; (4) Development of an interactive web platform using the Flask framework, with front-end interfaces built using HTML, CSS, JavaScript, and Layui for data overview, query, statistical visualization, and download.Results:A fully functional and locally deployable database website was successfully constructed. The system supports efficient data querying, dynamic statistical visualization (including pie charts for species distribution and bar charts for top proteins and drug classes), and data export. The platform integrates multi-source resistance data and offers a user-friendly interface for real-time data access and analysis, providing a practical tool for researchers and clinicians.Conclusion:This study developed a comprehensive and scalable database for drug-resistant bacteria, facilitating the centralized management and analysis of AMR data. The database aids in understanding resistance mechanisms, guiding clinical treatment, and informing public health strategies. Future work will focus on expanding data coverage, incorporating AI-driven analysis, and enhancing system automation and security to further support the global fight…
subject: Articles
  topic: section
subject: Drug-resistant
  topic: keywords
subject: bacteria
  topic: keywords
subject: Database
  topic: keywords
subject: Antibiotic resistance
  topic: keywords
subject: python
  topic: keywords
creator: Xuanyu Pan
creator: Liying Yang
creator: Yuxiao He
creator: jianping Zhou
creator: Yongjian Zhou
creator: Tingwei Lyu
creator: Xingrao Li
creator: Yani Yang
creator: Yanling Hu
rights: Copyright (c) 2025 iCell
date: 2025-12-05
publication date: 2025-12-05
doi: 10.71373/VTXZ5661

A bibliometric analysis of RNA methylation in prostate cancer and its complications from 2003 to 2024
type: item
description: Background: RNA methylation has emerged as an active research field in prostate cancer (PCa) and its complications, while few bibliometric analyses have been performed. This study aims to analyze the development trends and hotspots of RNA methylation research to provide a comprehensive and objective overview of the current search state in this field of prostate cancer using bibliometric methods. Methods: The articles and reviews regarding RNA methylation in PCa within 2003 to 2024, and its complications were from the Web of Science Core Collection databases. A retrospective bibliometric analysis and science mapping were performed using the CiteSpace software, the VOSviewer software, and the R package “bibliometrix” to plot the knowledge maps and predict the hotspots and trends. Results: One hundred and twenty-two qualified records were retrieved. The annual publications gradually increased over the past 22 years. These publications mainly came from 19 countries led by China and 138 institutions. Among top 10 funders, 8 were Chinese, with NSFC supporting 68 studies. The most common keywords were “prostate cancer”, “expression”, “messenger-RNA”, “progression”, “methyltransferase METTL3 promotes”, “N6-methyladenosine”, and “promotes”. Keyword trends shifted from “m6A”/“METTL3” to “m6A modification”/“androgen receptor”/“metastasis”. N6-methyladenosine (m6A) within prostate cancer and its associated complications is an emerging frontier, with “m6A score” and “cellular senescence” as current priorities. Conclusion: This research represents the inaugural bibliometric examination of the application of RNA methylation in PCa, utilizing three acknowledged bibliometric tools. It offers an impartial overview and holistic direction for subsequent pertinent studies.
subject: Editorial
  topic: section
subject: bibliometric
  topic: keywords
subject: RNA methylation
  topic: keywords
subject: bibliometric analysis
  topic: keywords
subject: prostate cancer
  topic: keywords
subject: m6A
  topic: keywords
creator: Huifeng Wang
creator: Xiangyuan Wei
creator: Junjie Ye
creator: Bo Xie
creator: Junling Shan
creator: Xiangfa Deng
creator: Die Zhang
creator: Lu Xu
creator: Zengjing Liu
creator: Nada M.A. Hassan
creator: Chao Liu
creator: Yu`ang Mao
rights: Copyright (c) 2025 iCell
date: 2025-12-31
publication date: 2025-12-31
doi: 10.71373/LHKI4844

From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models
type: item
description: Recent advances in single-cell profiling and artificial intelligence have enabled the construction of virtual cells, computational entities that simulate cellular states and responses with unprecedented resolution. Initiatives such as the Virtual Cell World Challenge highlight the potential of large AI models to move beyond annotation toward predictive and generative simulations. Yet, the next frontier lies in scaling from virtual cells to virtual organs, where thousands of cell types interact across spatial, temporal, and biophysical dimensions. This transition exposes major challenges: incomplete cellular atlases, limited integration of spatial and longitudinal data, difficulties in cross-scale modeling, and the lack of robust validation frameworks. Addressing these obstacles requires embedding biological priors into foundation models, developing multi-modal integration strategies, and adopting graph-based and hybrid mechanistic-statistical approaches. The emergence of digital twins--organ- or patient-specific replicas--illustrates how virtual organ models can inform drug discovery, predict toxicity, and guide precision medicine. Ultimately, the trajectory from virtual cells to virtual organs points toward the vision of a virtual human, enabling in silico experimentation at scale. Realizing this goal will demand not only technical breakthroughs but also collaborative validation, ensuring that medical AI navigates these deep waters toward safe and transformative clinical application.
subject: Reviews
  topic: section
subject: Virtual Cells; Virtual Organs; Medical AI Models; Digital Twins
  topic: keywords
creator: Haisong Qin
creator: Sanqi An
creator: Shuaiyi Liang
creator: Jiapeng Qu
creator: Lixin Liang
creator: Dingping Feng
creator: Ziyi Liu
creator: Ziyang Luo
creator: Xiaocong Kuang
creator: Xuyong Sun
rights: Copyright (c) 2025 iCell
date: 2025-09-05
publication date: 2025-09-05
doi: 10.71373/IQHA9494

JNK1 downregulation confers fibroblasts with a mammary epithelial cell fate
type: item
description: The fate of cells is determined by their specific transcription patterns. Previously, we demonstrated that chemical cocktails, including the single small molecule TGFβR1 inhibitor RepSox, can induce changes in transcription patterns and drive somatic cells to reprogram into chemically induced mammary epithelial cells (CiMECs) by downregulating TGFβR1 expression. However, the underlying mechanism of chemical induction for reprogramming remains unclear. Here, we employed single-cell sequencing (scRNA-seq and scATAC-seq) to uncover RepSox-induced reprogramming events and molecular trajectories and clarify the similarities and differences to the iMECs generated by chemical cocktails induction. Importantly, we found that JNK1 expression, a downstream gene of TGFβR1 signaling pathway, plays a critical role in iMEC conversion as its downregulation induces iMEC generation. Thus, the TGFβR1-JNK1 signaling may regulate fibroblast-to-mammary epithelial cell cell fate decision. These findings provide valuable insights into the mechanisms governing mammary epithelial cell fate decisions and may pave the way for developing novel strategies to generate a large number of milk-secreting mammary cells in vitro and facilitate mammary gland regeneration in vivo.
subject: Articles
  topic: section
subject: Somatic cell transdifferentiation
  topic: keywords
subject: Fibroblast-to-mammary epithelial cell
  topic: keywords
subject: Induced mammary epithelial cells (iMECs)
  topic: keywords
subject: TGFβR1-JNK1 signaling
  topic: keywords
creator: Dandan Zhang
creator: Liangshan Qin
creator: Quanhui Liu
creator: Shan Deng
creator: Zhigang Lei
creator: Longfei Sun
creator: Hong Pan
creator: Zhiqiang Wang
creator: Xiang Yuang
creator: Guodong Wang
creator: Ben Huang
rights: Copyright (c) 2025 iCell
date: 2025-10-24
publication date: 2025-10-24
doi: 10.71373/PMKJ7669

Resveratrol Oligomers Trans-gnetin H Promotes Reactive Oxygen Species Generation and Rictor Degradation to Suppress Cancer Cell Viability
type: item
description: The function of resveratrol as an antioxidant is to scavenge reactive oxygen species (ROS) in the body. The effects of resveratrol on cellular activity have been widely reported. Trans-gnetin H is a trimer of resveratrol that is found in seeds of peonies. However, there is not enough research on the cellular effects of trans-gnetin H, and it is not clear if it has the same physiological functions as resveratrol. This study tries to answer this question by investigating the effect of trans-gnetin H on ROS, mammalian target of rapamycin (mTOR), cell viability, autophagy, and ferroptosis in several cancer cell lines. Trans-gnetin H was found to regulate cell viability, cell proliferation, and autophagy, but it did not affect ferroptosis. Molecular experiments showed that these effects were brought about via significant promotion of ROS generation and suppression of mTOR activation in an ROS-dependent manner. Further mechanistic experiments showed that trans-gnetin H inhibits mTOR activation by inducing FBXW7-mediated ubiquitination and degradation of Rictor, a key component of mTORC2. Thus, trans-gnetin H appears to control tumor cell viability by inhibiting activation of the mTORC1 and mTORC2 pathways through a novel regulatory mechanism involving the ROS-Rictor signaling axis.
subject: Articles
  topic: section
subject: Trans-gnetin H
  topic: keywords
subject: ROS
  topic: keywords
subject: mTOR
  topic: keywords
subject: Rictor
  topic: keywords
subject: FBXW7
  topic: keywords
subject: antitumor treatment
  topic: keywords
creator: Shiyang Lou
creator: Ruixu Zhang
creator: Chao Xia
creator: Yining Zheng
creator: Miaomiao Zhu
creator: Dingping Feng
creator: Lu Deng
rights: Copyright (c) 2025 iCell
date: 2025-08-26
publication date: 2025-08-26
doi: 10.71373/UGHK9221

Annotation and target analysis of human endogenous retroviruses
type: item
description: Endogenous retroviruses (ERVs) are important regulatory elements in the human genome. They are involved in the regulation of host gene expression and disease progression through long terminal repeats (LTR) and coding domains (gag, pol, env). Methods: Based on the implicit Markov model, this study integrated LTRharvest and LTRdigest software, combined with 55 ERV-related protein domain databases, systematically annotated ERVs elements in the human genome (GRCh38.p14), and analyzed their potential targets and functions by using STRING, GO and KEGG. Results: A total of 47,666 HERVs candidate sequences (11.05% of the genome) were identified in this study, of which 605 were complete structures, mainly concentrated in chromosomes 1 and 3. It was found that env accounted for the least among the three protein structures. Potential target genes in the upstream and downstream 20kb range of LTR were screened, and core targets such as histone family genes H4C6 and H2BC12 and immune-related genes TLR2 and CCR5 were found to be involved in disease regulation through chromatin remodeling or immune pathways. Enrichment results were significantly associated with nucleosome assembly, innate immune response, and cancer-related pathways (e.g., herpes simplex virus infection, systemic lupus erythematosus). Conclusion: This study constructed a comprehensive HERVs annotated database, revealing the potential regulatory ability of LTR, providing a theoretical basis for the application of HERVs in cancer, autoimmune diseases and evolutionary research, and laying a foundation for the development of targeted therapy strategies.
subject: Articles
  topic: section
subject: human endogenous retroviruses
  topic: keywords
subject: Long terminal repeat
  topic: keywords
subject: regulation mechanism
  topic: keywords
creator: Huilin Wei
creator: Boying Liang
creator: Chunlan Jiang
creator: Tingwei Lyu
creator: Wen Li
creator: Qiuxia Meng`
creator: Tengyue Yan
creator: Xuanyu Pan
creator: Yuxiao He
creator: Jianping Zhou
creator: Yanling Hu
rights: Copyright (c) 2025 iCell
date: 2025-05-14
publication date: 2025-05-14
doi: 10.71373/PVSC2027

Exosomal miRNAs in Inflammatory Diseases
type: item
description: Exosomal miRNAs are a type of non-coding RNA molecules present in exosomes. MiRNAs are involved in the regulation of a variety of physiological and pathological processes by transmitting information between cells through the exosome, a carrier of intercellular communication. During inflammatory responses, exosomal miRNAs can be involved in regulating the activation of inflammatory cells and the release of inflammatory mediators, thus affecting the development of inflammatory diseases. Therefore, exosomal miRNAs may be promising biomarkers for monitoring disease progression based on their functions and changes. In addition, since exosome prevents miRNAs from being degraded by RNase, drug development targeting the release of exosomal miRNA contents lays the foundation for innovative targeted therapies in the future. This review focuses on exosomal miRNAs with the aim of combing and mastering its latest developments in the current research of inflammatory diseases.
subject: Reviews
  topic: section
creator: Sufan Guo
creator: Xiaoli Lou
creator: Liyuan Yang
creator: Li Zhou
creator: Yanqiang Hou
creator: SongGuo Zheng
rights: Copyright (c) 2025 iCell
date: 2025-04-08
publication date: 2025-04-08
doi: 10.71373/NDAN1471

Revealing PAM as a Prognostic Biomarker and Therapeutic Target in Clear Cell Renal Cell Carcinoma
type: item
description: Background: Accumulating evidence suggests that peptidylglycine α-amidating monooxygenase (PAM) is involved in vital physiological and pathological processes, including the development and progression of cancer. Nevertheless, the precise contributions of PAM-mediated pathways in clear cell renal cell carcinoma (ccRCC) remain poorly understood. Clarifying the role of PAM in ccRCC could yield novel insights into the disease's pathogenesis and offer potential therapeutic strategies. Methods: Using genome-wide association study (GWAS) data from the UK Biobank and whole-blood eQTL data, we screened for ccRCC-related genes and identified PAM as a potential oncogene. Bioinformatics analyses, including differential expression, prognostic, genomic, and methylation analyses, were conducted to characterize the role of PAM in ccRCC. In addition, functional pathways of PAM were explored using gene set enrichment analysis. The association between PAM expression, immune cell infiltration, and immunotherapy response was also evaluated. Subsequently, in vitro tumor phenotype experiments, such as cell viability, wound healing and modified Boyden chamber assays, were conducted to validate the bioinformatics predictions. Results: Our findings indicated that PAM expression was elevated in ccRCC tissues compared to adjacent normal ones, and is associated with unfavorable disease-free survival in ccRCC patients. Genomic alterations such as gene amplifications were detected in ccRCC, with PAM expression linked to multiple cancer pathways. Furthermore, PAM expression was positively correlated with immune cell infiltration and negatively with immune cell function in ccRCC. In vitro functional assays revealed that PAM downregulation reduced the proliferative and migratory capacity of ccRCC cells. Conclusions: Our studies reveal that PAM serves as a potential prognostic biomarker and therapeutic target in ccRCC. Further researches are warranted to validate its clinical utility and investigate …
subject: Articles
  topic: section
subject: renal cell carcinoma
  topic: keywords
subject: monooxygenase
  topic: keywords
subject: immunotherapy
  topic: keywords
subject: tumor microenvironment
  topic: keywords
subject: prognostic biomarker
  topic: keywords
creator: Xuanyu Wang
creator: Huahui Wu
creator: Yang Gao
creator: Haoran Liu
creator: Yangjun Zhang
creator: Chen Duan
creator: Xiongmin Mao
creator: Xiangyang Yao
creator: Kai Liu
creator: Bo Li
creator: Zhenzhen Xu
creator: Yihua Li
creator: Yan Gong
creator: Hua Xu
rights: Copyright (c) 2025 iCell
date: 2025-06-04
publication date: 2025-06-04
doi: 10.71373/YASO5977

The Mechanism of RNA Virus-mediated Tumorigenesis
type: item
description: Various RNA viruses could lead to tumorigenesis. Since these viruses belong to different genera, they have evolved distinct mechanisms of tumorigenesis, with unique interactions among key oncoproteins, oncogenes and hosts factors. This article reviews the tumorigenic mechanism of two well-known human RNA tumor viruses, and explores the potential tumorigenic effects of SARS-CoV-2, which is also an RNA virus.
subject: Reviews
  topic: section
subject: tumorigenesis
  topic: keywords
subject: RNA viruses
  topic: keywords
creator: Hubin Chen
creator: Jun Meng
creator: Lizhu Lin
rights: Copyright (c) 2024 iCell
date: 2024-11-01
publication date: 2024-11-01
doi: 10.71373/XEWE3812

Long COVID: Current Status, Challenges and Future Directions
type: item
description: Long COVID refers to the persistent symptoms and health issues experienced by patients following infection with the SARS-CoV-2. As the pandemic continues, research into Long COVID has gradually become a focal point for the global medical community. Current studies suggest that the pathogenesis of Long COVID may be closely related to factors such as immune response, inflammatory reactions, neurological damage, and microvascular lesions. The clinical manifestations are diverse and include, but are not limited to, fatigue, dyspnea, cognitive impairment, and mental health issues. Epidemiological studies show that there are significant differences in the incidence of Long COVID among different populations, and its effects can be long-lasting, severely affecting patients' quality of life. Currently, diagnostic methods for Long COVID are still under exploration, with common assessment tools involving clinical interviews and questionnaires. In terms of treatment strategies, while no specific treatments exist yet, a comprehensive management approach including symptom relief, rehabilitation training, and psychological support has shown some efficacy. By analyzing the latest research findings, this paper aims to provide references for clinical practice and future research, promoting further understanding and response to Long COVID.
subject: Reviews
  topic: section
subject: Long COVID; sequelae; SARS-CoV-2; clinical manifestations; mechanisms
  topic: keywords
creator: Liling Huang
creator: Tingting Huang
creator: Yongqian Liao
creator: Shaoling He
creator: Yanling Hu
creator: Jiannan Lv
creator: Yuluan Zhang
creator: Chengqiong Xu
rights: Copyright (c) 2024 iCell
date: 2024-11-01
publication date: 2024-11-01
doi: 10.71373/ELJT3204

RNA-Binding Proteins: Biological Mechanisms and Their Impact on Osteoporosis Development
type: item
description: Osteoporosis is a common bone disease characterized by reduced bone mass and increased fracture risk, largely driven by imbalances in osteoblast and osteoclast activity. Recent studies highlight the critical role of RNA-binding proteins (RBPs) in the regulation of bone metabolism, specifically in osteoblast and osteoclast function. These proteins have also been implicated in other bone diseases, suggesting a broader influence on skeletal health. This review explores the molecular mechanisms through which RBPs impact bone cells, discusses their potential as therapeutic targets in osteoporosis, and outlines future research directions and challenges in harnessing RBPs for clinical applications.
subject: Reviews
  topic: section
subject: RNA-binding proteins (RBPs); Osteoporosis; Biological Mechanisms
  topic: keywords
creator: Lizhu lin
creator: Peijun Xu
creator: Yanzhen Mo
creator: Runxian Tan
creator: Dongtao Li
rights: Copyright (c) 2024 iCell
date: 2024-11-01
publication date: 2024-11-01
doi: 10.71373/LIGH9788

Pan-Cancer Analysis for Identification of Tumor Antigens and Immune Subtypes in mRNA Vaccine Development
type: item
description: Purpose: Immunotherapy using vaccines proved effective for a variety of cancers, however, so far there is no vaccine against many types of cancers. The aim of this study was to identify potential tumor antigens for the development of antiall cancer mRNA vaccines and to map the immune landscape of pan-cancer to select suitable patients for vaccination. Methods: We collected normalized gene expression data and clinical data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). Gene Expression Profiling Interactive Analysis (GEPIA) version 2 was used to analyze the differential expression of cancer genes and to calculate the prognostic index of each selected antigen. We used the cBio Cancer Genomics Portal (cBioPortal) to visualize the gene alterations of potential tumor antigens in the TCGA data and the Tumor Immune Estimation Resource (TIMER) to analyze and visualize the association between immune infiltrates and expression level of the identified potent antigens. Consistency matrix analysis was used to identify corresponding gene modules and immune subtypes. Univariate and multivariate Cox regression as well as log-rank tests were used to assess the prognostic distinctions of the GBM immune subtypes. We used the reduce Dimension function of the Monocle package to visualize the distribution of immune subtypes across TCGA and CGGA patients. Results: Different cancer have different potential RNA Antigens. In GBM, seven tumor antigens (DIRAS3, EGFLAM, FUCA1, LILRB2, MPZL2, OSMR, and STC1) were identified as promising candidates for developing anti-GBM mRNA vaccines. Based on the immune gene expression profiles, GBM was classified into four immune subtypes (IS1–IS4). IS1 and IS4 were immunologically “hot,” which means they are more likely to benefit from vaccines. In addition, six immune hub genes (CLCF1, CD54, SOCS3, PLAUR, LIF, and BCL3) were identified as potential biomarkers for GBM vaccine effectiveness. Conclusions: Different cancer types …
subject: Articles
  topic: section
subject: mRNA vaccine
  topic: keywords
subject: glioblastoma multiforme (GBM)
  topic: keywords
subject: immune subtypes
  topic: keywords
creator: Yao Lin
creator: Xinyue Xu
creator: Ziyi Liu
creator: Hubin Chen
creator: Yi Bao
creator: Jun Meng
creator: Zheng Li
creator: Sanqi An
creator: Yueqi Li
creator: Xiaopeng Hu
creator: Jiemei Chu
rights: Copyright (c) 2024 iCell
date: 2024-11-01
publication date: 2024-11-01
doi: 10.71373/FEHU5094

TGDD: A Genomics-Based Database for Tumor Treatment Drugs
type: item
description: With advancements in cancer genomics, precision medicine is increasingly being applied in cancer treatment. However, the fragmentation of genetic mutation-related information and the lack of standardized interpretation tools present significant challenges to its effective implementation and clinical decision-making. To address these challenges, we have developed the Tumor Genomic Drug Database (TGDD), a comprehensive genomics-based cancer drug treatment resource. It integrates data on genes, molecular characteristics, cancer types, and therapeutic drugs from established cancer knowledge bases such as CIViC, COSMIC, and OncoKB, providing a unified and standardized clinical support tool. Currently, it encompasses 19,557 entries, including 391 gene datasets, 525 cancer types, 5,322 drug combinations, and 8,109 mutation coordinates. This resource assists clinicians in formulating personalized treatment plans and supports cancer research and drug development.
subject: Articles
  topic: section
subject: TGDD
  topic: keywords
subject: cancer genomics
  topic: keywords
subject: precision medicine
  topic: keywords
subject: driver mutations
  topic: keywords
subject: therapeutic drugs
  topic: keywords
creator: Wen Li
creator: Zhining Xiong
creator: Kangming He
creator: Zhining Zhang
creator: Huilin Wei
creator: Qiuxia Meng
creator: Tengyue Yan
creator: Yuxiao He
creator: Xuanyu Pan
creator: Yanling Hu
rights: Copyright (c) 2024 iCell
date: 2024-11-01
publication date: 2024-11-01
doi: 10.71373/UYBT8671

Prognostic Implications and Immune Infiltration in the Nod-like receptor signaling pathway: A Comprehensive Analysis across Pan-cancer
type: item
description: Purpose Nucleotide-binding oligosaccharide-like receptors (NOD) are pivotal molecules with crucial roles in the regulation of inflammation, tumor transformation, angiogenesis, tumor stem cells, and chemoresistance. This study aimed to assess the prognostic implications of NOD signaling in diverse cancer types and its relationship with immune infiltration. Methods Gene expression data from TCGA related to the NOD signaling pathway were integrated with clinical data. Prognostically relevant NOD pathway genes were analyzed using univariate and multivariate Cox regression and Kaplan-Meier survival analysis. The accuracy of our prediction model was validated through receiver operating characteristic (ROC) curve analysis.Single-cell analysis of genes associated with reduced survival in patients, and single-sample immunoinfiltration analysis revealed cell-level differences between different groups. Results Univariate Cox regression analysis, multivariate cox regression analysis and Kaplan-Meier analysis were used to identify prognostic genes in NOD pathway. TRAF5 is an important prognostic gene in multiple cancer types, and mutation analysis showed that patients with TRAF5 mutations had reduced survival. Immune infiltration analysis revealed differences in effector memory CD8 T cells and immature B cells between high- and low-risk groups, suggesting potential druggable targets. Single-cell analysis highlighted that reduced survival was associated with overexpression of TXN in both primary and metastatic tissues. Conclusion NOD signaling pathway, specifically TRAF5, plays a critical role in cancer prognosis across various cancer types. Immune infiltration disparities offer therapeutic opportunities, and TXN represents a promising target for novel anticancer treatments.
subject: Articles
  topic: section
subject: Nod-like receptor signaling pathway, pan-cancer, prognostic marker, Immune Infiltration
  topic: keywords
creator: Qiuxia Meng
creator: Tengcheng Que
creator: Tengyue Yan
creator: Zengjing Liu
creator: Huilin Wei
creator: Bo Xie
creator: Boying Liang
creator: Wen Li
creator: Die Zhang
creator: Nili Jiang
creator: Huifeng Wang
creator: Wenjian Liu
creator: Yanling Hu
rights: Copyright (c) 2024 iCell
date: 2024-11-01
publication date: 2024-11-01
doi: 10.71373/ZAGA8611