Zhongyi Yan

IFT172 is a core component of intraflagellar transport complex B (IFT‐B), and pathogenic IFT172 variants disrupt ciliary transport, receptor localization, and tissue‐specific signaling. This review summarizes evidence linking IFT172 dysfunction to neurological, retinal, skeletal, renal, and syndromic ciliopathy phenotypes, with emphasis on Bardet‐Biedl syndrome, Joubert‐spectrum disease, orofaciodigital syndrome, Mainzer–Saldino syndrome, and retinal degeneration. Current data support a primary role for IFT172 in IFT‐train assembly, tip remodeling, anterograde‐to‐retrograde transition, and cilia‐dependent signaling; downstream metabolic, oxidative, and inflammatory changes are interpreted as context‐dependent secondary modules rather than uniform linear cascades. We further discuss diagnostic interpretation and the practical boundaries of gene, splice‐correction, and pathway‐modulation strategies. By aligning molecular mechanisms with organ vulnerability, this review provides a concise framework for interpreting IFT172‐associated ciliopathies and for prioritizing future translational studies.

Introduction: DNGR-1 (CLEC9A), a C-type lectin receptor specifically expressed on human BDCA3+ and murine CD8α+/CD103+ conventional type 1 dendritic cells (cDC1), acts as a damage-associated molecular pattern (DAMP) receptor that recognizes necrotic cell-derived F-actin and promotes antigen cross-presentation. Despite the growing focus on DNGR-1 in immunotherapy research, no comprehensive, global bibliometric analysis spanning the full research history of DNGR-1 has been reported to date to delineate its global research trends, hotspots, and evolutionary trajectories. Methods: We retrieved 326 relevant DNGR-1 publications from the Web of Science Core Collection (WOSCC) and Scopus databases, covering the period from January 2008 to January 2026. Bibliometric visualization and analysis were performed using VOSviewer, CiteSpace, and Bibliometrix, with associated clinical trial data retrieved from PubMed to contextualize translational progress. Results: Annual publication output in this field has shown a steady upward trend, with the USA, the UK, and China ranking as the top three contributing countries. Monash University was the most productive institution (32 papers), Lahoud MH was the most prolific author (33 papers), and Sancho D was the most frequently co-cited researcher (228 citations). Frontiers in Immunology was the most prolific journal in this field. Thematic evolution in the field has shifted from an early focus on "C-type Lectins" and "myeloid cells" to recent research centered on "Adaptive Immunity", "Spatial Transcriptomics", and "Systems Biology", reflecting a clear evolution from foundational molecular mechanism research to cross-scale translational immunotherapy development. Conclusion: This study presents the first systematic bibliometric analysis of global DNGR-1 research, mapping the knowledge structure, research hotspots, and evolutionary trends of the field over the 18-year study window. Our integrated analysis of publication trends, clinical trial data, and research frontiers highlights the critical role of DNGR-1 in tumor immunotherapy and cDC1 functional regulation, providing theoretical and empirical insights for developing novel immunotherapies targeting the DNGR-1-cDC1 axis.

Revolutionized cancer research, enabling systematic biomarker discovery through multi-omics analyses (Goodwin et al., 2016). For example, single-cell RNA sequencing (scRNA-Seq) and spatial transcriptomics have enabled the resolution of tumor heterogeneity at subcellular levels (Longo et al., 2021;Gulati et al., 2025).These innovations underscore the shift from bulk tissue analysis to spatially and temporally resolved molecular profiling, which is critical for deciphering dynamic tumor-immune interactions (Hsieh et al., 2022). The integration of multi-omics data has accelerated the identification of biomarkers with clinical utility. Below, we contextualize the studies in this research topic within recent breakthroughs in oncology.Multi-omics integration allows a comprehensive view of the mechanisms underlying any disease. Ahmed N et al. characterized genetic and epigenetic changes in AML progression using Omni-C, ATAC-seq, and RNA-seq data. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples (Ahmed et al., 2024). Uveal melanoma (UVM) is the most common primary intraocular malignancy. However, treatment outcomes are unsatisfactory, and the long-term prognosis remains dismal. To address this challenge, Zhang M et al. employed bioinformatics and identified that two TRP channel-related long noncoding RNAs (TCRLs), AC092535.4 and LINC01637, could serve as novel prognostic biomarkers for UVM and may present potential therapeutic targets (Zhang et al., 2024). Using multi-omics data, Zhao Y et al. found that FANCI is significantly upregulated in multiple tumor types. High FANCI expression correlates with poor prognosis in specific cancers and is associated with higher immune cell infiltration and tumor mutation burden. FANCI emerges as a promising biomarker for cancer prognosis and diagnosis, with potential as a novel therapeutic target (Zhao et al., 2025).Kinesin family proteins and metabolic reprogramming are emerging as central players in tumor progression (Chen et al., 2024b). The expression of KIF18B was correlated with immune infiltration in the tumor microenvironment. KIF18B is a key factor affecting the prognosis of glioblastoma (GBM) patients, and its targeting may provide a new therapeutic method for GBM patients (Su et al., 2025). Yang B et al. demonstrated that KIF18B correlated with hepatocellular carcinoma progression potentially via activation of the Wnt/β-catenin-signaling pathway, suggesting its broader role in epigenetic regulation (Yang et al., 2020). Targeting metabolic enzymes like GFPT1 has gained traction. In vivo, silencing of GFPT1 attenuated the immune escape of breast cancer cells by reducing PD-L1 levels (Tang et al., 2024). These findings align with Liang et al.'s observation that high GFPT1 level was associated with increased cytoplasmic translation, activation of oncogenic pathways, and infiltration of M2 macrophages, indicating that GFPT1 may be a novel prognostic biomarker and an indicator of chemotherapy response in invasive breast carcinoma (Liang et al., 2024).Immune-related signatures are reshaping therapeutic strategies. Ni D, et al. constructed an 8-gene prognostic model (AK2, CXCL11, TYK2, ANGPT4, IL20RA, MET, ENPP6, and CA12) in pancreatic ductal adenocarcinoma (PDAC), enhancing prognostic accuracy and potentially therapeutic decision-making in PDAC, offering valuable insights for evaluation in clinical practice (Ni et al., 2024). Wan C, et al. found that high MARCKS, MACC1, and GRB10 expression in their tumors correlated with poorer survival rates in endometrial cancer patients. High expression of NINJ2 correlated with higher survival rates and higher sensitivity to radiation therapy (Wan et al., 2025).Immunotherapy based on immune checkpoint inhibitors (ICIs) has become a prominent focus in the development of novel anti-tumor drugs and has been tested in several human clinical trials. Thus, an immune-related gene prognostic index (IRGPI) was developed by Chen D et al., providing a systematic analysis of distinct and molecular characteristics in papillary renal cell carcinoma (PRCC) (Chen et al., 2024a). This research topic exemplifies the power of multi-omics approaches in discovering novel biological biomarkers and unraveling the molecular complexity of cancer. By identifying biomarkers that reflect tumor biology, immune interactions, and therapeutic vulnerabilities, these studies pave the way for precision oncology. As we advance, integrating multi-omics data with AI and innovative therapeutics will be pivotal in transforming cancer care, ultimately improving survival and quality of life for patients worldwide.Keywords: biomarkers, multi-omics data, therapeutic targets, immunotherapy, cancer

Background: Clec9a (also named as DNGR-1), is a restricted DAMPs (Damage-Associated Molecular Patterns) receptor that recognizes cell death signals expressed on human BDCA3 ⁺ DC and mouse CD8α ⁺ and CD103 ⁺ DC. With the rapid advancement of immunology, numerous studies of Clec9a have been published. However, no relevant bibliometric studies have been published to date. This study aims to provide a comprehensive understanding of the current state of research and identify potential new research directions via a bibliometric analysis of Clec9a. Methods: Publications related to Clec9a from 2008 to 2024 were retrieved by the Web of Science Core Collection (WoSCC). The three software (VOSviewer, CiteSpace and Bibliometrix) are to assess the contribution and co-occurrence relationships of various countries, institutions, journals and authors, and to explore research hotspots and future trends in this field. Results: A total of 240 English articles from various countries published between 2008-2024 were identified, with an increasing trend in the number of publications each year. The top three contributing countries were USA, Australia, and UK, accounting for 22.50%, 21.25%, and 20.00%, Respectively. The University of Melbourne contributed the most with 32 papers (13.33%). Lahoud, Mireille H, with 32 articles, ranked first amongst authors in terms of publications and Sancho, D. with 188 co-citations, was the amongst co-cited authors. Frontiers in Immunology was influential academic journal in Clec9a research. The aggregation and identification of key nodes in the co-citation network revealed a shift in the focus of Clec9a research. Initially, the hotspot areas were primarily ”antibody formation,” ”systems biology,” ”BDCA3,” and ”langerin,” while in recent years, the focus has shifted to ”vaccines” and ”autophagy.” Conclusion: This is the first bibliometric study to provide a comprehensive summary of the research trends and developments of Clec9a. Currently, the most active frontiers focus on better understanding the role and clinical applications of Clec9a in tumor immunotherapy and DC-targeted vaccination in viral diseases.

Apelin (APLN) is an endogenous ligand of the G protein‐coupled receptor APJ (APLNR). APLN/APLNR system was involved in a variety of pathological and physiological functions, such as tumorigenesis and development. However, its prognostic roles in patients with central nervous system (CNS) cancers remain unknown. The present study was designed to explore the expression profile, prognostic significance, and interaction network of APLN/APLNR by integrating data from Oncomine, GEPIA, LOGpc, STRING, GeneMANIA, and immunohistochemical staining. The results demonstrated that APLN and APLNR mRNA expression were significantly increased in CNS cancers, including both low‐grade glioma (LGG) and glioblastoma (GBM), when compared with normal CNS tissues. The high APLN, but not APLNR, expression was significantly correlated with overall survival (OS), recurrence free survival (RFS), and progression free survival (PFS) of LGG patients. However, neither APLN nor APLNR expression was significantly related to prognostic value in terms of OS, disease free interval (DFI), disease specific survival (DSS), or progression free interval (PFI) for GBM patients. Additionally, immunohistochemistry staining confirmed the increased APLN expression in tissues of LGG patients with grade II than grade I. These results showed that an elevated APLN level could predict poor OS, RFS, and PFS for LGG patients, and it could be a promising prognostic biomarker for LGG.

Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.

TXLNA (taxilin alpha), a binding partner of the syntaxin family, was identified as a key factor in the coordination of intracellular vesicle trafficking and highly expressed in various tumor cells. However, the accurate relation between TXLNA and tumorigenesis and progression of pancreatic adenocarcinoma (PAAD) is still unclear. The present study was designed to examine the expression profile of TXLNA and explore its prognostic significance in PAAD patients and the possible molecular regulatory mechanism by analyzing a series of data from databases, including GEPIA, LOGpc, STRING, and GeneMANIA. The results indicate that TXLNA mRNA and protein were remarkably increased in PAAD tissues compared with normal pancreatic tissues. The high TXLNA expression was significantly correlated with superior overall survival (OS), disease‐free interval (DFI), disease specific survival (DSS), and progression‐free interval (PFI) for PAAD patients. In summary, high TXLNA expression could predict favourable OS, DFI, DSS, and PFI for PAAD patients, and it might be as potential prognostic biomarkers and targets for PAAD.

The newly discovered extracellular death factor (EDF) is a pentapeptide with the sequence NNWNN in Escherichia coli. It was reported that it participated in the cell death process mediated by toxin-antitoxin system mazEF. Reactive oxygen species (ROS) are recently considered as common factors for bactericidal antibiotics-mediated cell death. Previous study indicated that EDF could scavenge hydroxyl radicals and might act as a signal molecule with dual effects, “death” and “survival”. But the structure-activity relationship of EDF and the effects of EDF on the activity of antibiotics remain unclear. In the present study, our results indicated that tryptophan could be the key residue to the hydroxyl radicals-scavenging activity of EDF, and EDF could protect Escherichia coli from killing by bactericidal antibiotics, but not by DNA-damaging or bacteriostatic antibiotics. Our results could provide novel evidence to understand the role of EDF in drug-resistance.