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Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.
Endometrial cancer (EC) is the most common gynaecological malignancy worldwide, yet the prognosis for advanced and recurrent disease remains poor, highlighting the need for improved diagnostic, prognostic, and therapeutic decision-making frameworks. Conventional approaches, including histopathology, imaging, and single-layer molecular profiling, provide essential clinical information but may not fully capture EC's biological heterogeneity, especially within clinically challenging No Specific Molecular Profile (NSMP) and mismatch repair-deficient (MMRd) subgroups. Artificial intelligence (AI) and machine learning (ML) provide powerful approaches to analyse complex, high-dimensional datasets generated by multi-omics profiling, histopathology, imaging, and clinical records.In this review, we synthesize the latest evidence on AI-driven multi-omics research in EC, encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, single-cell profiling, and spatial transcriptomics. Unlike other reviews that focus solely on AI, omics, or imaging, we integrate molecular, imaging, histopathological, and computational perspectives to underscore their collective impact on precision oncology in EC. We subsequently explore applications in molecular subtyping, predicting survival and recurrence, modelling treatment responses, discovering immunotherapy biomarkers, and identifying drug targets. Public resources such as The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumour Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO), cBioPortal, Human Protein Atlas, GTEx, and UCSC Xena have enabled large-scale reproducible analyses. However, challenges such as cohort heterogeneity, batch effects, ethnic underrepresentation, missing annotations, and the need for external validation remain significant hurdles.We then discuss the progression from conventional ML methods to deep learning architectures, including convolutional neural networks, transformers, graph neural networks, and multimodal fusion models applied to histopathological, radiological, and multi-omics data. Landmark models such as EndoNet, EndoRisk, and HECTOR illustrate the potential of AI-enabled approaches to support EC grading, molecular inference, lymph node metastasis prediction, and recurrence-risk stratification. Finally, we examine key translational barriers, including class imbalance, interpretability, data harmonization, regulatory requirements, and the implementation gap between high-performing retrospective models and routine clinical deployment. Ultimately, this review underscores how bridging these multi-modal computational approaches paves the way for precision oncology in EC.
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A Single-Cell Transcriptome Atlas of Epithelial Subpopulations in HPV-Positive and HPV-Negative Head and Neck Cancers.
Persistent infection with HPV causes nearly 5% of all cancers worldwide, including cervical and oropharyngeal cancers. Compared to HPV-negative (HPV-) head and neck squamous cell carcinomas (HNSCCs), HPV-positive (HPV+) HNSCCs exhibit a significantly improved treatment response; however, established treatment regimens were largely developed for HPV- disease. Effectively de-escalating therapy and optimizing treatment protocols to minimize toxicity for both HPV+ and HPV- tumors has been variably successful, in part due to the heterogeneity of cellular subpopulations. Single-cell RNA sequencing (scRNAseq) has primarily been used to define immune cell populations rather than the cell type of origin, epithelial cells. To address this, we analyzed published scRNAseq data of HPV+ and HPV- HNSCCs to distinguish epithelial tumor cell populations as a function of HPV status. We identified the transcriptome signatures, ontologies, and candidate biomarkers of newly identified epithelial subpopulations with attention to those that are shared or enriched in HPV+ or HPV- HNSCCs. We hypothesize that distinct epithelial cell populations and reprogramming in HPV- versus HPV+ HNSCC represent important components of the pro-tumor environment. These are described here as a foundation for the identification of new epithelial-cell-specific biomarkers, effectors, and candidate targets for optimizing the treatment of HNSCC.
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A single cell atlas of frozen shoulder capsule identifies features associated with inflammatory fibrosis resolution.
Frozen shoulder is a spontaneously self-resolving chronic inflammatory fibrotic human disease, which distinguishes the condition from most fibrotic diseases that are progressive and irreversible. Using single-cell analysis, we identify pro-inflammatory MERTKlowCD48+ macrophages and MERTK + LYVE1 + MRC1+ macrophages enriched for negative regulators of inflammation which co-exist in frozen shoulder capsule tissues. Micro-cultures of patient-derived cells identify integrin-mediated cell-matrix interactions between MERTK+ macrophages and pro-resolving DKK3+ and POSTN+ fibroblasts, suggesting that matrix remodelling plays a role in frozen shoulder resolution. Cross-tissue analysis reveals a shared gene expression cassette between shoulder capsule MERTK+ macrophages and a respective population enriched in synovial tissues of rheumatoid arthritis patients in disease remission, supporting the concept that MERTK+ macrophages mediate resolution of inflammation and fibrosis. Single-cell transcriptomic profiling and spatial analysis of human foetal shoulder tissues identify MERTK + LYVE1 + MRC1+ macrophages and DKK3+ and POSTN+ fibroblast populations analogous to those in frozen shoulder, suggesting that the template to resolve fibrosis is established during shoulder development. Crosstalk between MerTK+ macrophages and pro-resolving DKK3+ and POSTN+ fibroblasts could facilitate resolution of frozen shoulder, providing a basis for potential therapeutic resolution of persistent fibrotic diseases.
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Investigation of the pan-cancer property of SDC1 and its expression pattern affected patients' overall survival for breast cancer.
Syndecan-1 (SDC1) is a transmembrane heparan sulfate proteoglycan that functions as a receptor in the extracellular matrix and plays a role in intercellular communication, cell proliferation, angiogenesis and metastasis. However, the effect of SDC1 expression on different cancers is controversial. In this study, we used The Cancer Genome Atlas (TCGA) dataset indicating that SDC1 was significantly upregulated in a wide range of cancers compared with normal tissue but was downregulated in kidney renal clear cell carcinoma (KIRC), kidney chromophobe (KICH), Kidney papillary cell carcinoma (KIRP) and Pheochromocytoma and Paraganglioma (PCPG). The expression of SDC1 was significantly associated with breast cancer subtype and this gene was found to be associated with overall survival (OS) in most analyzed cancers. From mechanistic perspective, SDC1 expression levels were correlated with DNA methylation, immune cell infiltration and tumor cell stemness in multiple cancer types based on TCGA dataset. We further investigated and validated these findings in breast cancer, SDC1 expression was significantly correlated with stem cell markers which were mainly expressed in tumor cells and stromal cells but not immune cells in the Tumor Immune Single-cell Hub 2 (TISCH2) dataset. In vivo validation, 708 breast tumors tissue microarray (TMA) analysis indicated that SDC1 protein expression in tumor cells was linked to overall survival in breast cancer patients (HR:1.54 (1.01, 2.35), P = 0.04), after adjusting for factors such as age, menopausal status, tumor characteristics, nodal involvement, TNM stage, vascular invasion, chemotherapy, and radiotherapy. In subtype analysis, SDC1 expression in tumor cells was associated with OS in the luminal subtype (HR: 1.82 (1.02, 3.25), P = 0.04), but not in human epidermal growth factor receptor 2 (HER2)-positive or triple-negative subtypes. In vitro validation, reducing SDC1 expression inhibited cell proliferation and suppressed cancer stemness biomarker expression in breast cancer cell lines. This study indicated that SDC1 expression exhibits varying impacts in pan-cancers, however, the higher SDC1 expression in breast tumor cells, but not in stromal cells, correlated with increased mortality, particularly in luminal molecular subtype, which might be a potential prognostic marker and therapeutic target for breast cancer therapy, especially in Luminal subtype breast cancer.
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CD4+ conventional T cells-related genes signature is a prognostic indicator for ovarian cancer.
INTRODUCTION: It is believed that ovarian cancer (OC) is the most deadly form of gynecological cancer despite its infrequent occurrence, which makes it one of the most salient public health concerns. Clinical and preclinical studies have revealed that intratumoral CD4+ T cells possess cytotoxic capabilities and were capable of directly killing cancer cells. This study aimed to identify the CD4+ conventional T cells-related genes (CD4TGs) with respect to the prognosis in OC. METHODS: We obtained the transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD4TGs were first identified from single-cell datasets, then univariate Cox regression was used to screen prognosis-related genes, LASSO was conducted to remove genes with coefficient zero, and multivariate Cox regression was used to calculate riskscore and to construct the CD4TGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. The role of riskscore has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. A risk signature with 11 CD4TGs in OC was finally established in the TCGA database and furtherly validated in several GEO cohorts. RESULTS: High riskscore was significantly associated with a poorer prognosis and proven to be an independent prognostic biomarker by multivariate Cox regression. The 1-, 3-, and 5-year ROC values, DCA curve, nomogram, and calibration results confirmed the excellent prediction power of this model. Compared with the reported risk models, our model showed better performance. The patients were grouped into high-risk and low-risk subgroups according to the riskscore by the median value. The low-risk group patients tended to exhibit a higher immune infiltration, immune-related gene expression and were more sensitive to immunotherapy and chemotherapy. DISCUSSION: Collectively, our findings of the prognostic value of CD4TGs in prognosis and immune response, provided valuable insights into the molecular mechanisms and clinical management of OC.
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Microplastics and nanoplastics-related genes signature predicts prognosis in pancreatic ductal adenocarcinoma and functional validation of interleukin 1 alpha.
BACKGROUND: Microplastics and nanoplastics (MNPs), as emerging environmental pollutants, have garnered significant attention from the global scientific community due to their potential threats to human health, particularly their association with the occurrence and development of cancer. The goal of our study is to create a predictive marker for pancreatic ductal adenocarcinoma (PAAD) based on MNPs-related genes, with the purposes of predicting survival outcomes and assessing the tumor immune microenvironment. METHODS: Using multi-cohort data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC), we assessed the association between MNPs and PAAD prognosis through the Xiantao Academic (https://www.xiantao.love/). The development of a prognostic signature was followed by an assessment of its significance through the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and decision curve analysis (DCA). The validity of the risk model was confirmed through the ICGC and GSE71729 cohorts. The model was then assessed for levels of tumor immune infiltration. To explore MNPs-related genes expression characteristics within immune cells in PAAD, we performed single-cell RNA sequencing and spatial transcriptomics analysis through the Sparkle Platform (https://grswsci.top/). Finally, in vitro experiments were conducted to investigate the biological function of interleukin 1 alpha (IL1A). RESULTS: A four-gene signature comprising XDH, IL1A, KIF20A, and ASPM, based on MNPs, was developed to stratify PAAD patients into two distinct risk groups. The high-risk group showed a significantly poorer prognosis. A similar trend was verified in the external cohorts ICGC and GSE71729. The signature risk score affected immune cell infiltration in the PAAD microenvironment. The infiltration of B cells, CD8+ T cells, cytotoxic cells, immature dendritic cells (iDCs), mast cells, plasmacytoid dendritic cell (pDC), T cells, Tem cells, T follicular helper (TFH) cells, and T helper 17 (Th17) cells had a positive correlation with the low-risk group. In contrast, high-risk patients tended to have increased number of T helper (Th2) cells and higher expression of SIGLEC15, CD274, IGSF8. Knockdown of IL1A in PAAD cells inhibited their tumor proliferation ability in vitro. CONCLUSIONS: Using MNPs-related genes, we built a prognostic model for PAAD, revealing that patients with high-risk scores are likely to have a worse prognosis. This model is designed to develop personalized treatment strategies tailored to the specific needs of each patient, thereby improving clinical outcomes for PAAD patients. Furthermore, IL1A could be a promising therapeutic candidate for PAAD.
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Spatial transcriptomics reveals human cortical layer and area specification.
The human cerebral cortex is composed of six layers and dozens of areas that are molecularly and structurally distinct1-4. Although single-cell transcriptomic studies have advanced the molecular characterization of human cortical development, a substantial gap exists owing to the loss of spatial context during cell dissociation5-8. Here we used multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based nucleus segmentation, to examine the molecular, cellular and cytoarchitectural development of the human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing more than 18 million single cells, spans eight cortical areas across seven developmental time points. We uncovered the early establishment of the six-layer structure, identifiable by the laminar distribution of excitatory neuron subtypes, 3 months before the emergence of cytoarchitectural layers. Notably, we discovered two distinct modes of cortical areal specification during mid-gestation: (1) a continuous, gradual transition observed across most cortical areas along the anterior-posterior axis and (2) a discrete, abrupt boundary specifically identified between the primary (V1) and secondary (V2) visual cortices as early as gestational week 20. This sharp binary transition in V1-V2 neuronal subtypes challenges the notion that mid-gestation cortical arealization involves only gradient-like transitions6,10. Furthermore, integrating single-nucleus RNA sequencing with MERFISH revealed an early upregulation of synaptogenesis in V1-specific layer 4 neurons. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This study establishes a spatially resolved single-cell analysis paradigm and paves the way for the construction of a comprehensive developmental atlas of the human brain.
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Single-cell RNA sequencing reveals the developmental program underlying proximal-distal patterning of the human lung at the embryonic stage.
The lung is the primary respiratory organ in human, in which the proximal airway and the distal alveoli are responsible for air conduction and gas exchange, respectively. However, the regulation of proximal-distal patterning at the embryonic stage of human lung development is largely unknown. Here we investigated the early lung development of human embryos at weeks 4-8 post fertilization (Carnegie stages 12-21) using single-cell RNA sequencing, and obtained a transcriptomic atlas of 169,686 cells. We observed discernible gene expression patterns of proximal and distal epithelia at week 4, upon the initiation of lung organogenesis. Moreover, we identified novel transcriptional regulators of the patterning of proximal (e.g., THRB and EGR3) and distal (e.g., ETV1 and SOX6) epithelia. Further dissection revealed various stromal cell populations, including an early-embryonic BDNF+ population, providing a proximal-distal patterning niche with spatial specificity. In addition, we elucidated the cell fate bifurcation and maturation of airway and vascular smooth muscle progenitor cells at the early stage of lung development. Together, our study expands the scope of human lung developmental biology at early embryonic stages. The discovery of intrinsic transcriptional regulators and novel niche providers deepens the understanding of epithelial proximal-distal patterning in human lung development, opening up new avenues for regenerative medicine.
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Identification of angiogenesis-related genes signature for predicting survival and its regulatory network in glioblastoma.
Glioblastoma (GBM) is notorious for malignant neovascularization that contributes to undesirable outcome. However, its mechanisms remain unclear. This study aimed to identify prognostic angiogenesis-related genes and the potential regulatory mechanisms in GBM. RNA-sequencing data of 173 GBM patients were obtained from the Cancer Genome Atlas (TCGA) database for screening differentially expressed genes (DEGs), differentially transcription factors (DETFs), and reverse phase protein array (RPPA) chips. Differentially expressed genes from angiogenesis-related gene set were extracted for univariate Cox regression analysis to identify prognostic differentially expressed angiogenesis-related genes (PDEARGs). A risk predicting model was constructed based on 9 PDEARGs, namely MARK1, ITGA5, NMD3, HEY1, COL6A1, DKK3, SERPINA5, NRP1, PLK2, ANXA1, SLIT2, and PDPN. Glioblastoma patients were stratified into high-risk and low-risk groups according to their risk scores. GSEA and GSVA were applied to explore the possible underlying GBM angiogenesis-related pathways. CIBERSORT was employed to identify immune infiltrates in GBM. The Pearson's correlation analysis was performed to evaluate the correlations among DETFs, PDEARGs, immune cells/functions, RPPA chips, and pathways. A regulatory network centered by three PDEARGs (ANXA1, COL6A1, and PDPN) was constructed to show the potential regulatory mechanisms. External cohort of 95 GBM patients by immunohistochemistry (IHC) assay demonstrated that ANXA1, COL6A1, and PDPN were significantly upregulated in tumor tissues of high-risk GBM patients. Single-cell RNA sequencing also validated malignant cells expressed high levels of the ANXA1, COL6A1, PDPN, and key DETF (WWTR1). Our PDEARG-based risk prediction model and regulatory network identified prognostic biomarkers and provided valuable insight into future studies on angiogenesis in GBM.
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A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation.
PURPOSE: Nonsense-mediated RNA decay (NMD), a surveillance pathway for selective degradation of aberrant mRNAs, is associated with cancer progression. Its potential as a predictor for aggressive hepatocellular carcinoma (HCC) is unclear. Here, we present an innovative NMD risk model for predicting HCC prognosis. METHODS: The Cancer Genome Atlas (TCGA) data of 374 liver HCC (LIHC) and 50 normal liver samples were extracted. A risk model based on NMD-related genes was developed through least absolute shrinkage and selection operator Cox (LASSO-Cox) regression of the LIHC-TCGA data. Prognostic validation was done using GSE54236, GSE116174, and GSE76427 data. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the model. We also constructed nomograms for survival prediction. Tumor immune infiltration was evaluated using the CIBERSORT algorithm, and the tumor cell phenotype was assessed. Finally, mouse experiments verified UPF3B knockdown effects on HCC tumor characteristics. RESULTS: We developed a risk model based on four NMD-related genes (PABPC1, RPL8, SMG5, and UPF3B) and validated it using GSE54236, GSE116174, and GSE76427 data. The model effectively distinguished high- and low-risk groups corresponding to unfavorable and favorable HCC outcomes. Its prognostic prediction accuracy was confirmed through time-dependent ROC analysis, and clinical-use nomograms with calibration curves were developed. Single-cell RNA sequencing results indicated significantly higher expression of SMG5 and UPF3B in tumor cells. Knockdown of SMG5 and UPF3B inhibited HCC cell proliferation, invasion, and migration, while affecting cell-cycle progression and apoptosis. In vivo, UPF3B knockdown delayed tumor growth and increased immune cell infiltration. CONCLUSION: Our NMD-related gene-based risk model can help identify therapeutic targets and biomarkers for HCC. Additionally, it assists clinicians in predicting the prognosis of HCC patients.