Haitao Wang

Breast cancer liver metastasis (BCLM) represents a major clinical challenge. Characterizing the differences between primary and metastatic tumors could help identify therapeutic targets for preventing and treating metastasis. Through single-cell transcriptomic sequencing of matched primary tumors and BCLMs from 13 treatment-naive patients, we uncovered a fundamental paradox in metastatic progression: BCLMs exhibited a highly immune-infiltrated microenvironment characterized by cytotoxic T-cell expansion, inflammatory macrophage polarization, and depletion of regulatory T cells, yet they successfully evaded immune-mediated destruction. Metastatic cells retained extensive clonal and genomic diversity from their primary tumors, suggesting a polyclonal metastatic process rather than a stringent metastatic “bottleneck.” Instead, cancer cells featured a metabolic adaptation mechanism in which elevated glutathione (GSH) metabolism in the liver fosters glutamine addiction, creating liver metastasis susceptibility. Integrative regulatory network analyses pinpointed nuclear factor erythroid 2–related factor 2 (NRF2) as a key regulator associated with GSH metabolism, and functional experiments demonstrated that GSH promoted tumor stemness and attenuated CD8+ T cell–mediated antitumor immunity in an NRF2-dependent manner. Inhibition of NRF2 and GSH reduced liver metastasis in vivo and was associated with enhanced antitumor immune responses. Together, this study provides comprehensive single-cell characterization of the immunometabolic landscape in treatment-naive BCLM and reveals a potential model in which metastatic success in immunocompetent tissues depends on tumor cell–intrinsic metabolic adaptation. These findings provide insights into breast cancer liver metastatic progression and suggest NRF2 and GSH inhibition as a promising therapeutic strategy that could restore immune surveillance in liver metastases. Significance: Paired single-cell analyses reveal that breast cancer liver metastasis overcomes an immunostimulatory microenvironment by undergoing metabolic adaptation, identifying a mechanism enabling metastatic fitness that can be targeted to disrupt the metastatic cascade.

Background: Identifying robust predictive biomarkers for immune checkpoint blockade (ICB) remains a challenge. While alternative splicing (AS) shapes tumor immunity, the lack of large-scale, consensus-based datasets constrains the characterization of ICB-specific splicing landscapes. Methods: We developed TCASIA (the Cancer Alternative Splicing Immunotherapy Atlas), harmonizing >4000 samples across 29 cohorts. A rigorous “2-out-of-4” multi-algorithm consensus framework (rMATS, MAJIQ, SUPPA2, and SplAdder) with a fuzzy matching strategy was used to establish a high-confidence AS landscape. Results: TCASIA provides a robust toolkit linking splicing perturbations with clinical phenotypes (response, survival, and immune infiltration). Using this platform, we recapitulated the established FAS exon 6 skipping mechanism and identified IL7R exon 6 skipping as a functionally supported candidate associated with reduced CD8 ⁺ T-cell infiltration and poor survival in melanoma. Conclusions: TCASIA provides an analytical atlas for investigating splicing-mediated immune evasion and prioritizing isoform-specific candidates for further functional and clinical validation in the context of ICB treatment.

Epidemiological studies have indicated a strong link between metabolic disease and an elevated risk of cancer. However, it has not been directly replicated in animal models, nor has its specific underlying mechanism been clarified. From our previous research, liver-specific SIRT1 knockout (LKO) mice developed hyperglycemia within two months and developed fatty liver with whole-body insulin resistance around nine months of age. When the mice’s age extended to one year, they presented surprisingly higher lung tumor vulnerabilities in contrast to wild type as determined by macroscopic observation and histological examination. Interestingly, all lung tumors in these mice were classified as lung adenocarcinomas. Microarray analysis revealed elevated levels of MDM2, an oncoprotein, leading to the downregulation of its target genes such as p21 and GADD45. In vitro, the disruption of MDM2 leads to impaired cell cycle checkpoints and increased cell proliferation, which is accompanied by genomic instability, eventually causing full transformation in normal lung epithelia. In addition, the heat shock factor 1 (HSF1) transcription factor was found to regulate MDM2 directly and would decrease in the nucleus under high glucose conditions. Knockdown of HSF1 in the bronchial epithelial cell line (NL20) can increase MDM2 expression and cell proliferation. Human lung adenocarcinomas also displayed elevated MDM2 levels, with a correlation between MDM2 expression and lung cancer survival rates. Collectively, our findings suggest that liver-specific SIRT1 knockout-induced hyperglycemia promotes spontaneous lung adenocarcinomas through the HSF1-MDM2 pathway. Hyperglycemia may represent an underexplored cause of lung adenocarcinomas through impairment of cell cycle checkpoints, providing valuable insights for lung cancer prevention and future precision medicine.

Pro-or anti-tumoral outcomes depending on environmental cues [3] . Harnessing this adaptability offers promising avenues for developing novel immunotherapeutic strategies. Dong and Zhao et al. highlighted the critical interaction between NK cells and head and neck squamous cell carcinoma (HNSCC), wherein NK cells exert direct cytotoxic and immunomodulatory effects. However, HNSCC evades NK cell surveillance through mechanisms such as downregulation of NKG2D ligands and the establishment of an immunosuppressive TME. Cetuximab, an anti-EGFR monoclonal antibody, enhances NK cell-mediated antibody-dependent cellular cytotoxicity and immunogenic modulation, improving outcomes in EGFR-overexpressing HNSCC. Emerging approachesincluding CAR-NK cells, cytokine-primed NK cells, and bispecific engagers-show clinical potential. However, challenges such as TGF-β-mediated suppression and metabolic competition within the TME must be addressed to optimize NK cell-based therapies. Combining EGFR inhibition with NK cell activation may offer a synergistic strategy for HNSCC treatment. In colorectal cancer (CRC), impaired NK cell infiltration and function within the TME have been observed, although the underlying mechanisms remain incompletely understood. Li and Wang et al. reported reduced SMAD4 expression in tumor-infiltrating NK cells, with SMAD4 overexpression enhancing NK cell cytotoxicity against CRC cells. Mechanistically, SMAD4 upregulates NKG2D expression via YTHDF2, a key downstream effector that regulates m6A-modified RNA metabolism. These findings establish the SMAD4-YTHDF2 axis as a critical regulator of NK cell anti-tumor activity and suggest SMAD4 as a potential therapeutic target for enhancing NK cell-based immunotherapy in CRC.Similarly, tumor-associated macrophages (TAMs) exhibit plasticity that allows them to facilitate immune evasion and promote tumor metastasis. TAMs can release cytokines that inhibit effector immune cells and attract additional immunosuppressive cells to the TME, highlighting their role as key regulators of tumor immune evasion [4] . Lung cancer (LC) remains a leading global malignancy, where tumor-associated macrophages (TAMs) is implicated in tumor progression and metastasis through their dynamic polarization into pro-tumor (M2-like) or anti-tumor (M1-like) phenotypes. The plasticity of TAMs, governed by complex signaling crosstalk within the TME, offers therapeutic opportunities to reprogram M2-like macrophages toward tumor-suppressive M1-like states. Li and Yuan et al's works highlight synthetic and natural compounds that modulate these pathways, while nanotechnology-enabled drug delivery systems (NDDSs) enhance precision and efficacy by targeting TAMs with minimal off-target effects. This review systematically examines the signaling networks between TAMs and LC cells, surveys cutting-edge TAM-reprogramming therapeutics, and evaluates nano-based strategies for combination therapy. Despite promising preclinical progress, we also address the challenges and future directions for translating TAM-targeted therapies into clinical practice. Liu et al.'s study demonstrated that SHP2 + TAMs serve as a theranostical target in nonsmall cell lung cancer (NSCLC). Through multiplex fluorescence analysis of 79 NSCLC specimens, we revealed that CD68 + SHP2 + TAMs are predominantly enriched in tumor regions (vs stroma) and correlate with poor overall survival (p < 0.05). Importantly, SHP2 expression was higher in M2-like (CD68 + CD206 + ) than M1-like (CD68 + CD86 + ) macrophages, with strong positive correlations between CD68 + SHP2 + and CD68 + CD206 + subsets across all TME compartments (p < 0.0001). These findings position SHP2 as a key regulator of M2-like TAM polarization and suggest that SHP2 inhibition could reprogram the immunosuppressive TME, offering a novel immunotherapeutic strategy for NSCLC. The spatial distribution patterns of SHP2 + TAM subpopulations provide critical insights for developing targeted therapies and predictive biomarkers.The occurrence and development of tumor plasticity is synergistically driven by the interaction between intrinsic regulation and the external microenvironment, in which the intrinsic regulatory network of tumor cells themselves plays a core leading role, and its importance is comparable to the interaction effect between the tumor microenvironment and immune cells. [5] Moreover, the key molecules in the regulatory network can govern the formation of malignant phenotypes and the establishment of environmental adaptability of tumor cells by regulating biological processes such as metabolism, epigenetics and cell cycle, and they also serve as core targets for the development of precision targeted therapy strategies. [6] Dysregulated lipid metabolism is a key hallmark of prostate adenocarcinoma progression. [7] Wang and Ying et al. study elucidates the critical role of stearoyl-CoA desaturase (SCD) in prostate adenocarcinoma (PRAD) progression through comprehensive multi-omics analyses. We identified elevated SCD expression in PRAD, associated with promoter hypermethylation and the L134V mutation, which correlated with increased tumor aggressiveness. Intriguingly, SCD levels showed positive associations with CD8+ T cell and macrophage infiltration, suggesting immunometabolic crosstalk in the tumor microenvironment. Single-cell RNA sequencing revealed cell-type specific SCD expression patterns, while functional enrichment analyses linked SCD to lipid/cholesterol biosynthesis and cell cycle pathways. Crucially, experimental validation demonstrated that SCD overexpression enhanced PRAD cell proliferation and invasion, while its knockdown exerted opposite effects. These findings position SCD as both a promising therapeutic target and prognostic biomarker in PRAD, particularly given the tumor's unique dependence on lipid metabolism. Tumor plasticity and immune microenvironment remodeling are the core drivers of gastric cancer...

Biomedical research data visualization faces several challenges, including insufficient expertise and fragmented methodologies, which severely limit research efficiency and result quality. FigureYa is a standardized visualization framework composed of 317 modular R/python scripts, rather than a standalone software or desktop application. It covers key domains such as expression profiling, immune analysis, survival analysis, and single‐cell data visualization. Based on the concept of “replace data and use,” FigureYa significantly lowers the technical threshold, allowing researchers to generate high‐quality charts without requiring an extensive programming background. Compared to generic online R code snippets, FigureYa offers rigorously developed, thoroughly validated, and biologically contextualized visualization modules originally written by the author team. Each script includes version‐matched environments, example datasets, and detailed annotations, providing clear advantages in automation, reproducibility, and scientific professionalism, thereby providing a standardized visualization solution for complex biomedical data. This innovative tool optimizes research time allocation, promotes interdisciplinary collaboration, accelerates scientific discovery and clinical translation, and provides robust data visualization support for biomedical research.

The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.