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PubMed2026

Screening and identification of key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods.

OBJECTIVE: Radiotherapy (RT) plays a crucial role in the comprehensive treatment of rectal cancer. However, the impact of radiotherapy on the tumor microenvironment (TME), especially its effect on immune cell infiltration and immune-related gene expression, has not been fully studied. This study aims to screen and analyze key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods for the purpose of identifying potential biomarkers and providing new insights for the personalized therapy of rectal cancer. METHODS: Using data from the Public Gene Expression Database (GEO) and the Cancer Genomics Database (TCGA), the impact of radiotherapy on the immune microenvironment of rectal cancer was explored using bioinformatics tools. Through screening differentially expressed genes (DEGs), correlation analysis, TIMER database analysis, immune infiltration score, and correlation analysis between key genes and prognosis, the effects of radiotherapy on the immune microenvironment of rectal cancer were investigated. RESULTS: Totally 7 upregulated and 4 downregulated differentially expressed genes were identified, among which MASP1, LTK, SLC9A3R2 were negatively correlated with myeloid suppressor cell infiltration (MDSCs), while ZP2 was positively correlated. The expression of MASP1 and SLC9A3R2 was closely related to the level of immune cell infiltration and played significant roles in the immune microenvironment. High expression of MASP1 was significantly correlated with survival benefits from immune checkpoint inhibitor therapy, while SLC9A3R2 was closely related to the efficacy of PD-L1 inhibitors and CTLA4 inhibitors. CONCLUSIONS: MASP1 and SLC9A3R2, as two key genes that may be related to the immune microenvironment of rectal cancer radiotherapy, deserve further exploration of their roles in the mechanism. The combination of radiotherapy and immunotherapy holds promising prospects in the treatment of rectal cancer, and exploration of related mechanisms will provide new strategies and targets for the treatment of various tumors and rectal cancer.

Bioinformatics↗
PubMed1979

Morphologic changes in arterial grafts in rabbit ears.

The morphology of auto- and allografted segments of rabbit central ear arteries was studied at various times after grafting. Autografts showed thrombosis only in the immediate postoperative period. Autografts developed intimal thickenings whose cellular elements at all stages were almost exclusively myointimal in type. Their medial smooth muscle cells were viable at all stages. Allografts frequently thrombosed within 8 weeks of grafting. Intimal thickenings that developed in the first 6 weeks in allografts mainly contained infiltrating hematogenous cells with few myointimal cells. Immune cells infiltrated all of the layers of allograft walls, and the smooth muscle cells of their medias showed increasingly severe degeneration and by 8 weeks had completely disappeared. In long established allografts, the intima was extremely thick and contained myointimal cells and fibrous tissue. Their medias were fibrosed. In long standing allografts, immune cell infiltration was no longer present. The thrombosis of allografted arteries that occurred within 8 weeks of grafting was related to immunologic events observed within the vessel grafts. Differences between myointimal cells and smooth muscle cells with regard to their morphology and orientation were identified. A possible origin of myointimal cells from endothelial cells is suggested.

Animals↗
PubMed2025

Progressive T cell exhaustion and predominance of aging tissue associated macrophages with advancing disease stage in penile squamous cell carcinoma.

Penile squamous cell carcinoma (PSCC) is a rare malignancy with limited understanding of the tumor immune microenvironment (TIME). The interplay between PSCC and the immune system across disease progression and HPV infection status remains poorly characterized. This study aims to assess the TIME changes from localized to advanced disease and between HPV-positive versus negative tumors to identify potential immune evasion mechanisms in advanced PSCC. scRNA-seq was performed on ten PSCC tissue samples from penile, lymph node and distant metastatic sites with four matched penile and lymph node samples to understand the cellular heterogeneity within PSCC tumors. Analysis of immune cell populations and transcriptional hallmarks were performed stratified by localized (pT1-3, N0) versus advanced (N1-3, M0 or any N, M1) disease states and HPV infection status. We observed significant differences in immune cell infiltration between localized and advanced PSCC disease states and by HPV status. Advanced disease states demonstrated an exhausted immune phenotype, characterized by terminally exhausted CD8+ T cells, M2-like macrophages and hypoxic signature, while localized disease states demonstrated an active innate immune system characterized by increased DCs. HPV-negative tumors displayed low immune cell infiltration while HPV-positive tumors demonstrated an immune exhausted phenotype. These findings offer valuable insights into the evolving PSCC immune landscape, paving the way for the development of potential therapeutic approaches for advanced PSCC.

Humans↗
PubMed2026

Construction of a new predictive model in head and neck squamous cell carcinoma based on the investigation of extracellular matrix-associated genes.

A key aspect influencing immune cell infiltration is the composition of the extracellular matrix (ECM). Therefore, investigating the association between ECM-associated proteins and immune cell infiltration is key for the identification of new biomarkers to distinguish 'immune-hot' solid tumors and predict patient prognosis. A total of 513 head and neck squamous cell carcinoma (HNSCC) cases as training samples from The Cancer Genome Atlas and an additional 270 as testing samples from the Gene Expression Omnibus were obtained for use in the present study. Using a single-sample Gene Set Enrichment Analysis method, the 513 training samples were divided into Cluster 1 and Cluster 2. Subsequently, the present analysis uncovered 1,573 differentially expressed genes distinguishing the two clusters. After performing an intersection analysis with 751 ECM-associated genes, 103 differentially expressed ECM-associated genes were identified. Least absolute shrinkage and selection operator-Cox and multivariate Cox regression analyses were employed to identify candidate ECM risk genes (P<0.05) and to construct a predictive model. Finally, a nomogram and a three gene (cerebellin 2, galectin-10 and cathepsin G) predictive model were developed. Therefore, the present prognostic risk score model can evaluate the immune infiltration, predict the prognosis of HNSCC, and potentially guide more personalized immunotherapy interventions.

extracellular matrix↗
PubMed2026

A novel glutamine metabolism-based classification system for characterizing the heterogeneity of hepatocellular carcinoma.

BACKGROUND: Glutamine dependence is a hallmark of tumor cell metabolism, and further molecular classification based on glutamine metabolism in patients with hepatocellular carcinoma (HCC) may provide clinical value. This study thus comprehensively examined the patterns of HCC-specific alterations in glutamine metabolism. METHODS: Consensus clustering analysis was conducted on samples from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset based on glutamine metabolism-related genes, which was validated in the GSE76427, the Liver Cancer-France (LICA-FR) cohort, and the Liver Cancer-Japan (LIRI-JP) cohort from the ICGC. Somatic mutation features were evaluated with the Maftools package in R. The activity of oncogenic pathways was estimated via gene set enrichment analysis (GSEA) or single-sample GSEA (ssGSEA). The tumor microenvironment was analyzed using both the CIBERSORT algorithm (for immune cell infiltration estimation) and the ESTIMATE algorithm (for stromal and immune score calculation). Drug sensitivity and immune checkpoint blockade (ICB) response were also analyzed, for which a classifier was built via least absolute shrinkage and selection operator (LASSO). Immunohistochemistry (IHC) was performed to validate the protein expression levels of key differentially expressed genes (DEGs). Intracellular glutamine content under different glutamine concentrations was measured. The viability of HCC cell lines under varying glutamine concentrations was assessed via Cell Counting Kit-8 (CCK-8) assays. Cell migration and invasion were evaluated through Transwell assays, and protein expression was analyzed via Western blotting. RESULTS: HCC samples were classified into two glutamine metabolism-based clusters, with cluster 1 having a more advanced stage of disease and shorter survival than cluster 2. A higher frequency of genetic mutations and stronger activation of oncogenic pathways was found in cluster 1. There were substantial differences in immune cell infiltration and stromal scores between clusters 1 and 2. Cluster 1 exhibited significantly higher infiltration of immunosuppressive cells and lower stromal scores compared to cluster 2. Cluster 1 had a stronger response to ICB due as indicated by a higher tumor mutation burden (TMB) and T cell-inflamed gene expression profile score, immune checkpoints, and Tumor Immune Dysfunction and Exclusion (TIDE)-predicted data. Moreover, the LASSO classifier accurately differentiated the two clusters. The DEGs between the two clusters were validated in clinical samples. IHC confirmed the differential expression of glutamine metabolism-related genes in HCC samples. CCK-8 assays showed no significant effect of glutamine concentration on cell proliferation. However, Transwell assays revealed that glutamine deprivation (0.2 mM) reduced migration and invasion, while high-glutamine conditions (10 mM) promoted them. Western blotting showed increased expression of metabolism-related proteins under high-glutamine conditions and reduced expression under deprivation. CONCLUSIONS: Altogether, these findings indicate the involvement of glutamine metabolism in HCC and may help inform patient stratification and the formulation of precision therapeutics for this population.

Hepatocellular carcinoma (HCC)↗
PubMed2025

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies. OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data. METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes. RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts. CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

Humans↗
PubMed2025

The MTORC1 signaling pathway related gene POLR3G serves as a potential prognostic biomarker in Hepatocellular Carcinoma.

This study aims to investigate the prognostic significance and potential biological functions of the MTORC1 signaling pathway-associated gene POLR3G in Hepatocellular carcinoma (HCC). A prognostic risk model for HCC was developed by integrating HCC-related datasets and associated clinical data obtained from The Cancer Genome Atlas (TCGA) database. The GSVA website was employed to analyze the model genes across pan-cancer datasets, focusing on copy number variations (CNV), single nucleotide variations (SNV), methylation differences, drug sensitivity and immune cell infiltration profiles. Subsequently, we examined the expression levels and prognostic significance of POLR3G in HCC. Utilizing Spearman correlation analysis, we identified genes associated with POLR3G. Furthermore, Gene Set Enrichment Analysis (GSEA) was employed to elucidate the potential signaling pathways in which POLR3G may be involved. The relationship between POLR3G expression and immune cell abundance in HCC samples was assessed using the ssGSEA algorithm. Finally, the impact of POLR3G on HCC cell proliferation was validated through CCK-8 and EDU cell proliferation assays. Through univariate Cox regression analysis and LASSO regression analysis, we established a prognostic risk model for HCC comprising 13 genes. The analysis revealed that individuals categorized in the low-risk group had a markedly improved overall survival probability relative to those in the high-risk group. POLR3G exhibited a markedly elevated expression in HCC tissues when compared to adjacent normal tissues. The expression of POLR3G was correlated with tumor grade, and elevated POLR3G expression was associated with poor prognosis in HCC patients. Furthermore, the expression level of POLR3G was found to be correlated with the level of immune cell infiltration. Knockdown of POLR3G significantly inhibited the proliferative capacity of hepatocellular carcinoma cells. The findings suggest that POLR3G may serve as a potential biomarker influencing the prognosis of hepatocellular carcinoma patients by modulating the tumor immune microenvironment.

Humans↗
PubMed2026

Matrix Mechanics Governs Mechano-Metabolic Adaptation across Cancer Grades in Bladder Spheroids.

Extracellular matrix (ECM) mechanics is pivotal regulators of tumor progression, yet how viscoelasticity and matrix architecture converge to shape metabolic and invasive adaptation remains insufficiently defined. We postulate that mechanical stimuli from the ECM induce coordinated changes in adhesive and metabolic pathways, and that the nature of this independent mechano-metabolic pathway is conserved across benign, low-invasive, and high-invasive bladder cancer phenotypes. Therefore, we engineered collagen-hyaluronan hydrogels with tunable stiffness to recapitulate soft and rigid tumor microenvironments and profiled bladder cancer spheroids representing benign, low-invasive, and highly invasive states. Integrating hydraulic force spectroscopy, rheology, and molecular phenotyping, we show that matrix stiffening differentially reprograms spheroid architecture, motility, and adhesion- and metabolism-related gene expression. Spheroid behavior emerged from the interplay between intrinsic mechanical properties, matrix rheology, and molecular adaptation. HCV29 spheroids formed rigid, compact structures, relying on cell-matrix adhesion rather than metabolic or proteolytic remodeling. HT1376 spheroids activated glycolysis (HK2) and MMP-2-dependent ECM remodeling in soft matrices, but remained largely nonmigratory, indicating decoupling of invasive priming from motility. T24 spheroids were soft, deformable, and highly migratory in compliant matrices, integrating metabolic reprogramming, adhesion remodeling (E-/N-cadherin, SDC4), and radial collagen fiber alignment to drive invasion. Notably, canonical FAK/AKT/mTOR signaling was absent across all spheroids, while pS6 ribosomal protein and ILK indicated noncanonical, SDC4/integrin-ILK-dependent mechanotransduction supporting cytoskeletal dynamics, metabolism, and ECM remodeling. Collagen organization further differed across spheroid types, with dense, radially aligned fibers in HT1376, intermediate architecture in HCV29, and loose, disorganized networks in T24, closely matching their distinct migratory behaviors and cell-ECM interactions. These findings reveal stage-specific mechanometabolic strategies in bladder cancer, demonstrating how ECM mechanics and architecture jointly guide invasion, metabolic adaptation, and local immune modulation, including the regulation of immune cell infiltration and tumor immune evasion.

Humans↗
PubMed2026

IL1B-centered immune dysregulation involving IL7R, CCR7, ITGB2 and IRF1 across insomnia and inflammatory bowel disease.

BACKGROUND: Insomnia is a prevalent sleep disorder that strongly affects one's quality of life and physical well-being. Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the intestines, and a majority of IBD patients suffer from comorbid insomnia. However, the shared molecular features linking insomnia and IBD remain poorly characterized. METHODS: Common differentially expressed genes (DEGs) were identified in datasets of insomnia (GSE208668) and IBD (GSE179285) using the Limma package. Functional enrichment was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Protein-protein interaction (PPI) network construction and hub gene identification was subsequently performed. Furthermore, we validated the reliability of the hub genes using qRT-PCR and Enzyme-linked immunosorbent assay (ELISA). In addition, we constructed a TF-miRNA regulatory network of hub genes and assessed the abundance of immune cell infiltration in insomnia and IBD using CIBERSORT, EPIC, and xCell algorithms. Finally, we utilized the DsigDB to predict potential therapeutic candidates. RESULTS: The analysis revealed 75 upregulated and 32 downregulated common DEGs. Functional enrichment analysis revealed the inflammatory response and immune activation as pivotal drivers underlying the pathogenesis of both insomnia and IBD. Five hub DEGs, namely, IL1B, IL7R, CCR7, ITGB2, and IRF1, were subsequently screened and validated. The TF-miRNA-mRNA regulatory network consisted of 5 TFs, 14 miRNA nodes and 5 core mRNA nodes. Immune cell infiltration analysis revealed several patterns shared between insomnia and IBD. Additionally, 10 potential therapeutic drugs for insomnia and IBD were proposed. CONCLUSION: Integrative coexpression network analysis reveals convergent dysregulation of an IL1B-centered immune module (comprising IL7R, CCR7, ITGB2, and IRF1) across insomnia and IBD, a shared immune disturbance and candidate targets for simultaneous intervention upon further mechanistic validation.

Humans↗
PubMed2026

Mature Tertiary Lymphoid Structures in Breast Cancers Are Associated With Antitumor Immunity and Better Prognosis.

Tertiary lymphoid structures (TLSs) are immune cells accumulated in nonlymphoid tissues, with an inner core of B cells encompassed by T cells. The aim of this study was to evaluate the clinical importance of mature TLSs in breast cancer, including their association with immunotherapy response and their role in modulating the tumor immune microenvironment. We analyzed histopathological data of 726 consecutive primary breast cancers and transcriptomic data of 824 breast cancer samples from the publicly available The Cancer Genome Atlas database to estimate the clinical and immunological values of mature TLSs in breast cancer. Additionally, we utilized pretreatment transcriptomic data of 69 patients with breast cancer from the publicly available I-SPY2 clinical trial to investigate the relation between TLS-related gene signatures and patient responses to immune checkpoint inhibitors. The existence of mature TLSs was identified in &#x2053;5.6% (41/726) of all patients with breast cancer (hormone receptor-positive human epidermal growth factor receptor-2 negative (HR+HER2-): 0.92%; triple-negative breast cancer (TNBC): 14.96%; and human epidermal growth factor receptor-2 positive (HER2+): 10.98%) and was independently associated with improved recurrence-free survival after adjusting for subtypes, tumor-infiltrating lymphocyte levels, and tumor stage after the multivariable Cox regression analysis in our patient cohort. Notably, the presence of mature TLSs was related to immune cell infiltration in our breast cancer patient cohort. In line with these findings, TLS-related gene signatures analyzed through transcriptomic data reliably reflected the existence of mature TLSs and were related to better clinical responses to immune checkpoint inhibitors in patients with breast cancer. In conclusion, our findings show that mature TLS formation is linked with immune cell infiltration, contributes to a favorable prognosis, and may function as a potential complementary biomarker for immunotherapy response in breast cancer.

Humans↗
PubMed2025

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. METHODS: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. RESULTS: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. CONCLUSION: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Pulmonary Disease, Chronic Obstructive↗
PubMed2025

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.

OBJECTIVE: Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers. MATERIALS AND METHODS: In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. Disease Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis enrichment analyses were performed to explore differential gene functions and disease pathways. CIBERSORT was performed to calculate the immune cell infiltration of HCC and the correlation between diagnostic genes and immune cells. Cell experiments were performed to evaluate the function of R-spondin 3 (RSPO3) in HCC cells. Immunohistochemical staining was used to evaluate the protein expression of CD138, CD206 and iNOS. RESULTS: The results indicated that extracellular matrix protein 1 (ECM1), Niemann-Pick C1-Like 1 (NPC1L1) and RSPO3 were down-regulated in HCC compared with the normal group (p&#x2009;<&#x2009;0.05), which was validated in clinical tissue samples. Moreover, ECM1, NPC1L1 and RSPO3 had high diagnostic values (AUC > 0.75) for HCC in both training and test groups. Immuno-infiltration analysis revealed that ECM1 and RSPO3 were highly positively correlated with neutrophil and macrophage M2 levels, whereas they were negatively correlated with Tregs. RSPO3-si affected cell proliferation and apoptosis in HCC. Furthermore, RSPO3 exhibited a positive correlation with tumour progression, the proportion of plasma cells and M2 macrophages in mice, while showing a negative association with M1 macrophages. CONCLUSION: The present study identified ECM1, NPC1L1 and RSPO3 as new diagnostic biomarkers for HCC based on normal and diseased samples from HCC, meanwhile the pro-oncogenic function of RSPO3 and its regulation on immune infiltration have been confirmed.

Carcinoma, Hepatocellular↗
PubMed2026

Radiomics as a spatial context for treatment decision-making in head and neck cancer.

Radiomics has been widely explored as a non-invasive biomarker in head and neck squamous cell carcinoma (HNSCC), yet its clinical role remains unclear. Tissue-based biomarkers differ in their susceptibility to spatial sampling. Biomarkers such as PD-L1 expression, immune-cell infiltration, necrosis, and immune exclusion may exhibit substantial spatial heterogeneity, whereas HPV/p16 status and some genomic alterations are generally more stable across the tumor. Nevertheless, localized sampling may incompletely capture heterogeneity in selected clinical contexts. This mismatch becomes clinically relevant when treatment decisions, particularly for chemoradiotherapy, immunotherapy, or de-escalation, are based on potentially non-representative biopsy findings. In this narrative review, we argue that the role of radiomics is not to outperform established biomarkers, but to contextualize them by capturing spatial heterogeneity related to hypoxia, necrosis, stromal architecture, and immune exclusion. We synthesize current evidence linking radiomic features to these biological processes and map them to specific clinical decision points, including larynx preservation, immunotherapy stratification, and recurrence assessment. Rather than serving as a standalone predictor, radiomics may provide complementary spatial information that helps identify situations in which biopsy-derived biomarkers should be interpreted with caution. Although current evidence is largely retrospective, radiomics offers a pragmatic framework for integrating spatial information into biomarker-guided clinical workflows.

Journal Article↗
PubMed2025

Experimental study on the role and biomarker potential of CX3CR1 in osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a chronic joint disorder marked by progressive degeneration of articular cartilage and the formation of secondary osteophytes. Despite extensive research, the underlying molecular mechanisms remain poorly understood. This study aimed to identify OA-associated genes and elucidate the molecular pathways implicated, with the goal of discovering reliable diagnostic biomarkers. METHODS: The microarray dataset was retrieved from the Gene Expression Omnibus (GEO) and analyzed using R software to identify the signature gene, CX3CR1. Differentially expressed genes (DEGs) correlated with CX3CR1 were subsequently subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune infiltration analyses. A ceRNA regulatory network was also constructed. Vali-dation of CX3CR1 expression was conducted through qRT-PCR, Western blotting, and immunohistochemistry. RESULTS: CX3CR1 emerged as a candidate gene significantly associated with OA, exhibiting regulatory roles primarily in lipid metabolism-related and extra-cellular matrix-related biological processes and signaling cascades. The infiltration levels of immune cells, particularly activated mast cells, appeared to modulate OA progression. Both in vitro and in vivo experiments demonstrated elevated CX3CR1 expression in OA tissues relative to controls, with a robust positive correlation observed between CX3CR1 and MMP13 levels. CONCLUSION: CX3CR1 represents a potential biomarker for OA diagnosis and therapeutic targeting, exerting its effects by modulating lipid metabolism, extracellular matrix dynamics, and immune cell infiltration.

CX3C Chemokine Receptor 1↗
PubMed2026

The role of KIAA1467 in breast cancer: insights from pan-cancer and single-cell sequencing analysis.

BACKGROUND: Improving the response rate of single-agent immune checkpoint blockade (ICB) urgently requires the discovery of new therapeutic targets for combinatorial regimens. Analyses of tumor microenvironment (TME)-associated biomarkers have verified that KIAA1467 drives the formation of an immune-excluded, non-inflamed TME in breast cancer (BRCA). This study systematically explores the expression pattern, prognostic value, immune regulatory function, biological effects, and drug resistance relevance of FAM234B (also known as KIAA1467) in BRCA. METHODS: We performed pan-cancer survival analysis using The Cancer Genome Atlas (TCGA) datasets. Multi-omics bioinformatics analyses were conducted to evaluate KIAA1467 expression across malignancies. Single-cell RNA sequencing (scRNA-seq) data from GSE176078 was utilized to localize KIAA1467 expression at the cellular level. Immunohistochemistry and western blot assays validated KIAA1467 expression in BRCA clinical specimens. Correlation analyses were implemented to assess relationships between KIAA1467 expression, clinicopathological features, immune modulators, tumor-infiltrating immune cells, and p53 mutation status. Functional enrichment analysis uncovered relevant signaling pathways. Bioinformatic half maximal inhibitory concentration (IC50) prediction and in vitro cellular experiments were applied to evaluate associations between KIAA1467 and chemotherapeutic drug sensitivity. RESULTS: TCGA pan-cancer survival analysis demonstrated that elevated KIAA1467 expression significantly predicted shortened overall survival in BRCA and multiple other tumor types. KIAA1467 displayed distinct expression patterns across cancers, with prominent upregulation in BRCA. scRNA-seq confirmed enriched KIAA1467 expression within BRCA cells, and its upregulation in BRCA tissues was further verified by immunohistochemistry and western blot. High KIAA1467 expression was positively correlated with advanced tumor grade and lymphatic metastasis. KIAA1467 showed negative correlations with most immune modulators and core immune checkpoint molecules, as well as tumor-infiltrating immune cells in the TME, implying its potential function in tumor immune evasion. Low KIAA1467 expression was tightly linked to p53 mutations. Enrichment analysis indicated participation of KIAA1467 in epithelial-mesenchymal transition, apoptosis and cell cycle arrest. Furthermore, high KIAA1467 expression corresponded to higher estimated IC50 values of cisplatin, gefitinib, paclitaxel and gemcitabine, consistent with reduced chemosensitivity observed in vitro. CONCLUSIONS: This study reveals the multifaceted oncogenic role of KIAA1467 in BRCA. KIAA1467 participates in remodeling an immunosuppressive TME, correlates with malignant progression and chemoresistance, and may serve as a promising candidate target to optimize ICB-based combination therapy for BRCA. These findings offer new perspectives for the clinical treatment and comprehensive management of BRCA.

KIAA1467↗
PubMed2025

Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment.

BACKGROUND: Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity. METHODS: We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtypes with unique spatial patterns between tumor cells and T lymphocytes linked to certain molecular pathologic and prognostic features. T lymphocyte identification and phenotyping were achieved using multiplexed (multispectral) immunofluorescence. In a separate hepatocellular carcinoma cohort, we replaced the stromal component with specific immune cell types-CXCR3+CD68+ or CD8+-to profile their spatial relationships with CXCL9+CD68+ cells. RESULTS: Six unsupervised TIPC subtypes based on T lymphocyte distribution patterns were identified, comprising two cold and four hot subtypes. Three of the four hot subtypes were associated with significantly longer colorectal cancer (CRC)-specific survival compared to a reference cold subtype. Our analysis showed that variations in T-cell densities among the TIPC subtypes did not strictly correlate with prognostic benefits, underscoring the prognostic significance of immune cell spatial patterns. Additionally, TIPC revealed two spatially distinct and cell density-specific subtypes among microsatellite instability-high colorectal cancers, indicating its potential to upgrade tumor subtyping. TIPC was also applied to additional immune cell types, eosinophils and neutrophils, identified using morphology and supervised machine learning; here two tumor subtypes with similarly low densities, namely 'cold, tumor-rich' and 'cold, stroma-rich', exhibited differential prognostic associations. Lastly, we validated our methods and results using The Cancer Genome Atlas colon and rectal adenocarcinoma data (n = 570). Moreover, applying TIPC to hepatocellular carcinoma cases (n = 27) highlighted critical cell interactions like CXCL9-CXCR3 and CXCL9-CD8. CONCLUSIONS: Unsupervised discoveries of microgeometric tissue organizational patterns and novel tumor subtypes using the TIPC algorithm can deepen our understanding of the tumor immune microenvironment and likely inform precision cancer immunotherapy.

Humans↗
PubMed2026

Exploring the Genetic Link between Irritable Bowel Syndrome and Polycystic Ovary Syndrome: Bidirectional Mendelian Randomization and Machine Learning Approaches.

BACKGROUND: Research has shown a certain correlation between polycystic ovary syndrome (PCOS) and irritable bowel syndrome (IBS). The study aims to determine the directionality and underlying biological processes influencing the relationship between these two disorders. METHODS: We explored the causal relationship between IBS and PCOS by conducting a comprehensive bidirectional Mendelian randomization (MR) analysis using five different methods and conducted robustness assessments. We extracted differentially expressed genes from the IBS and PCOS datasets for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Additionally, we developed a protein-protein interaction (PPI) network and applied the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) methodologies to pinpoint key diagnostic markers. Diagnostic efficacy was further assessed through Receiver Operating Characteristic (ROC) curve analysis for selected genes. Finally, single-sample gene set enrichment analysis (ssGSEA) was carried out to examine immune cell infiltration in IBS and PCOS. RESULTS: MR analysis identified a causal effect of PCOS on IBS (IVW, OR = 1.034, 95% CI: 1.003-1.065, P = 0.029). Conversely, no relationship between IBS and PCOS was observed in the reverse analysis. Furthermore, integrative bioinformatics and machine learning analyses identified CD14 and CASP1 as key diagnostic biomarkers for both IBS and PCOS, which were significantly associated with immune cell infiltration. CONCLUSION: MR analysis has demonstrated a significant positive causal relationship between PCOS and IBS, though the reverse causality from IBS to PCOS appeared non-significant. The genes CD14 and CASP1 emerged as potential shared diagnostic markers between these two conditions.

Polycystic Ovary Syndrome↗
PubMed2024

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. METHODS: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. RESULTS: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. CONCLUSION: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Ferroptosis↗