Yuxin Wang

The JAK-STAT pathway transmits signals from cytokines and other extracellular regulators, providing a direct route by which cell-surface cues reach the nucleus. Although shaped by feedback and crosstalk, it is generally viewed as a linear cascade linking receptor engagement to kinase activation and transcriptional output. Type I interferons (IFN-Is) challenge this paradigm by driving distinct, even opposing, immune outcomes, depending on inflammatory context, yet how IFN-I signaling is organized to generate such plasticity has remained unknown. Here, we show that IFN-I signaling is governed by a preassembled STAT1-STAT2 reservoir that operates through a STAT2-gated “catch, hold and release” cycle. A 2.7 Å cryo-EM structure reveals an asymmetric STAT1-STAT2 scaffold that captures STAT1 for receptor-proximal activation. Limited IRF9 availability leaves activated STAT1/STAT2 heterodimers in excess of ISGF3, which persist as a reservoir that inflammatory TBK1/IKKε signaling mobilizes. TBK1/IKKε-dependent threonine phosphorylation of STAT2 releases STAT1 into a homodimer-driven inflammatory program. Genetic and pharmacologic perturbations of reservoir cycling reveal its requirement for antibacterial defense and timing-dependent inflammatory responses. These findings identify conformational reservoir cycling as a regulatory layer beyond the linear JAK-STAT cascade and explain how inflammatory context redirects IFN-I output.

Background: GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. Methods: Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. Results: First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). Conclusions: The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.

In cancer cells, endogenous or therapy-induced DNA damage leads to the abnormal presence of DNA in the cytoplasm, which triggers the activation of cGAS (cyclic GMP–AMP synthase) and STING (stimulator of interferon genes). STAT2 suppresses the cGAMP-induced expression of IRF3-dependent genes by binding to STING, blocking its intracellular trafficking, which is essential for the full response to STING activation. STAT2 reshapes STING signaling by inhibiting the induction of IRF3-dependent, but not NF-κB–dependent genes. This noncanonical activity of STAT2 is regulated independently of its tyrosine phosphorylation but does depend on the phosphorylation of threonine 404, which promotes the formation of a STAT2:STING complex that keeps STING bound to the endoplasmic reticulum (ER) and increases resistance to DNA damage. We conclude that STAT2 is a key negative intracellular regulator of STING, a function that is quite distinct from its function as a transcription factor.

Abundance of IL6 and COX2 in NSCLC cells and TPCA-1 inhibits their transcription.

TPCA-1 inhibits endogenous STAT3 activity with or without exogenous IL6 in NSCLC with EGFR mutation.

Abundance of IL6 and COX2 in NSCLC cells and TPCA-1 inhibits their transcription.

Combination of TPCA-1 increases sensitivity to gefitinib in TKI insensitive NSCLC.

Supplementary method and figure legends

Combination of TPCA-1 increases sensitivity to gefitinib in TKI insensitive NSCLC.