Reprogramming of the tumor microenvironment (TME) plays a critical role in gastric cancer (GC) progression and metastasis. However, the multidimensional features between primary tumors and organ-specific metastases remain poorly understood. In this study, we characterized the dynamic heterogeneity of GC from primary to metastatic stages. We identified seven major cell types and 27 immune and stromal subsets. Immune cells decreased, while immunosuppressive cells increased in ovarian and peritoneal metastases. A 30-gene signature for ovarian metastasis was validated in GC cohorts. Additionally, critical ligand-receptor interactions, including LGALS9-MET in liver metastasis and PVR-TIGIT in lymph node metastasis, were identified as potential therapeutic targets. Furthermore, CLOCK, a transcription factor, was associated with poor prognosis and influenced immune cell interactions and migration. Collectively, this study provides valuable insights into TME dynamics in GC and highlights potential avenues for targeted therapies.

Recently, many studies have shown that lncRNA can mediate the regulation of TF-gene in drug sensitivity. However, there is still a lack of systematic identification of lncRNA-TF-gene regulatory triplets for drug sensitivity. In this study, we propose a novel analytic approach to systematically identify the lncRNA-TF-gene regulatory triplets related to the drug sensitivity by integrating transcriptome data and drug sensitivity data. Totally, 1570 drug sensitivity-related lncRNA-TF-gene triplets were identified, and 16 307 relationships were formed between drugs and triplets. Then, a comprehensive characterization was performed. Drug sensitivity-related triplets affect a variety of biological functions including drug response-related pathways. Phenotypic similarity analysis showed that the drugs with many shared triplets had high similarity in their two-dimensional structures and indications. In addition, Network analysis revealed the diverse regulation mechanism of lncRNAs in different drugs. Also, survival analysis indicated that lncRNA-TF-gene triplets related to the drug sensitivity could be candidate prognostic biomarkers for clinical applications. Next, using the random walk algorithm, the results of which we screen therapeutic drugs for patients across three cancer types showed high accuracy in the drug-cell line heterogeneity network based on the identified triplets. Besides, we developed a user-friendly web interface-DrugSETs (http://bio-bigdata.hrbmu.edu.cn/DrugSETs/) available to explore 1570 lncRNA-TF-gene triplets relevant with 282 drugs. It can also submit a patient’s expression profile to predict therapeutic drugs conveniently. In summary, our research may promote the study of lncRNAs in the drug resistance mechanism and improve the effectiveness of treatment.

Tumor metastasis is the leading cause of death in cancer patients, with epithelial–mesenchymal transition (EMT) playing a key role. To systematically elucidate the cellular mechanisms and molecular networks through which EMT drives metastasis across cancers, this study integrated transcriptomic data from over 1.2 million single cells across 265 samples representing 12 primary epithelial cancers, constructing a comprehensive pan-cancer single-cell atlas covering diverse stages and metastatic states. By analyzing the metastatic features and interaction networks of malignant epithelial cells and cancer-associated fibroblasts (CAFs), we identified cancer-specific metastasis-related gene sets. Based on these genes, multiple machine learning algorithms were applied to build cancer-specific and cross-cancer metastasis prediction models, leading to the development of the metastasis prediction score (MPS) and global metastasis prediction score (GMPS). Both scores showed excellent predictive performance in independent test and external validation cohorts. MPS exhibited higher cancer specificity, whereas GMPS showed stronger cross-cancer generalization. Moreover, elevated MPS and GMPS reflected immunosuppressive tumor microenvironment features and were significantly associated with poor prognosis across multiple cancer types. Finally, through a drug repositioning framework, we identified several potential anti-metastatic compounds targeting the metastasis network, among which Fostamatinib demonstrated broad-spectrum therapeutic potential against metastasis in multiple cancers.

Melanoma shows heterogeneity across body sites like skin, acral skin, and the uvea, driven by molecular characteristics and genetic variations. However, comparative studies exploring the heterogeneity of melanoma across different anatomical sites remain limited, hindering a comprehensive understanding of its underlying biology. We proposed a research framework through bioinformatics to analyze the tumor ecosystems of cutaneous, acral, and uveal melanoma, from molecular characteristics to genetic variations at single-cell resolution. We found that oxidative phosphorylation (OXPHOS) is a critical driver of tumor cell evolution, with abnormal ribosomal gene and tumor suppressor expression observed in uveal melanoma (UM). Additionally, we screened for potential drug targets and drugs against tumor cells. In the immune microenvironment, acral melanoma (AM) and UM exhibit stronger immunosuppressive characteristics compared to cutaneous melanoma (CM). OXPHOS contributes to T cell cytotoxicity dysregulation in CM and AM, while interferon-γ is crucial in UM. Tumor cells may also induce T cell dysfunction through biological signals such as MIF-CD74 and HLA-E-NKG2A. This study offers valuable insights into melanoma heterogeneity, providing a comprehensive research framework for understanding the distinct molecular and immune characteristics of CM, AM, and UM, and potentially guiding the development of therapeutic strategies tailored to each melanoma subtype.

Esophageal squamous cell carcinoma (ESCC) progression involves dynamic cellular state transitions and tumor microenvironment remodeling, accompanied by extensive transcriptional regulation reprogramming. Here, we systematically mapped the TF-mediated regulatory landscape underlying ESCC progression at single-cell resolution by integrating stage-specific ESCC single-cell transcriptomic datasets comprising over 200,000 cells with TF–target interaction networks. Using a random walk algorithm combined with hypergeometric testing, we identified malignant progression-associated TFs (mpTFs) across multiple cell types and disease stages. Our analysis revealed extensive stage-dependent regulatory remodeling during ESCC progression. TCF4 was identified as an early-stage regulator associated with epithelial–mesenchymal transition activation and malignant invasive phenotypes. In immune lineages, BATF and IRF4 exhibited trajectory-associated activation during CD4⁺ T-cell differentiation and CD8⁺ T-cell exhaustion, suggesting critical roles in immunosuppressive T-cell state transitions. Additionally, mpTF-mediated remodeling of M2 macrophage subpopulations contributed to immunosuppressive tumor microenvironment formation during advanced ESCC progression. We further identified prognosis-associated cell-type-specific and shared mpTFs, including TFAP2C, which was associated with stabilized fibroblast and monocyte functional states and a less aggressive tumor microenvironment phenotype. Collectively, this study provides a comprehensive single-cell atlas of TF-mediated regulatory programs during ESCC progression and offers potential therapeutic targets for precision oncology.

Oncogenic driver mutations in non-small cell lung cancer (NSCLC) activate defined signaling pathways that sustain tumor growth and influence the immune landscape. Yet, how coordinated interactions among diverse cell populations within the tumor immune microenvironment (TIME) contribute to this process remains largely unresolved. To address this, we profiled approximately 200,000 single cells from 45 treatment-naïve NSCLC patients representing seven major driver mutations. This analysis uncovered five multicellular modules (CM1–5) with distinct functional properties, each linked to specific malignant regulatory programs. Among them, CM2 and CM5 exhibited pronounced invasive features and were associated with unfavorable clinical outcomes. CM2 was predominantly observed in EGFR- and MET-driven brain metastases and was defined by strong crosstalk between astrocytes and myofibroblasts. Factors such as SPP1, PTN, and PSAP, together with metabolic alterations, contributed to a microenvironment supportive of metastatic colonization in the brain. By contrast, CM5 was enriched in ROS1-, KRAS-, and EGFR-mutant tumors and consisted of diverse myeloid and endothelial subsets characterized by immunosuppressive and pro-angiogenic signaling, including MIF, GALECTIN, and RETN, collectively facilitating immune escape and vascular remodeling. We further constructed and validated a driver mutation-specific prognostic signature (DMSP.sig) model integrating receptor–ligand interactions and core transcription factors, which effectively stratified patient survival. Leveraging this model, we also identified potential therapeutic candidates linked to these prognostic features, highlighting opportunities for clinical intervention. In summary, our study delineates how oncogenic drivers give rise to distinct TIME architectures, providing a framework for prognostic assessment and precision immunotherapy in high-risk NSCLC.