Innate immune mechanisms initiate immune responses via pattern-recognition receptors (PRRs). Cyclic GMP-AMP synthase (cGAS), a member of the PRRs, senses diverse pathogenic or endogenous DNA and activates innate immune signaling pathways, including the expression of stimulator of interferon genes (STING), type I interferon, and other inflammatory cytokines, which, in turn, instructs the adaptive immune response development. This groundbreaking discovery has rapidly advanced research on host defense, cancer biology, and autoimmune disorders. Since cGAS/STING has enormous potential in eliciting an innate immune response, understanding its functional regulation is critical. As the most widespread and efficient regulatory mode of the cGAS-STING pathway, post-translational modifications (PTMs), such as the covalent linkage of functional groups to amino acid chains, are generally considered a regulatory mechanism for protein destruction or renewal. In this review, we discuss cGAS-STING signaling transduction and its mechanism in related diseases and focus on the current different regulatory modalities of PTMs in the control of the cGAS-STING-triggered innate immune and inflammatory responses.

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Human epidermal growth factor receptor 2 (HER2)-positive breast cancer and triple-negative breast cancer have their own genetic, epigenetic, and protein expression profiles. In the present study, based on bioinformatics techniques, we explored the prognostic targets of HER2-positive breast cancer from metabonomics perspective and developed a new risk score system to evaluate the prognosis of patients. By identifying the differences between HER2 positive and normal control tissues, and between triple negative breast cancer and normal control tissues, we found a large number of differentially expressed metabolic genes in patients with HER2-positive breast cancer and triple-negative breast cancer. Importantly, in HER2-positive breast cancer, decreased expression of metabolism-related genes ATIC, HPRT1, ASNS, SULT1A2, and HAL was associated with increased survival. Interestingly, these five metabolism-related genes can be used to construct a risk score system to predict overall survival (OS) in HER2-positive patients. The time-dependent receiver operating characteristic (ROC) curve analysis showed that the predictive sensitivity of the risk scoring system was higher than that of other clinical factors, including age, stage, and tumor node metastasis (TNM) stage. This work shows that specific transcriptional changes in metabolic genes can be used as biomarkers to predict the prognosis of patients, which is helpful in implementing personalized treatment and evaluating patient prognosis.

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The recent application of targeted immunotherapy has greatly improved the clinical outcomes of patients with lung adenocarcinoma (LUAD), but drug resistance continues to emerge, and to evaluate and to improve patient prognosis are arduous. The diagnostic and prognostic value of N6-methyladenosine (M6A) in LUAD has attracted increasing attention. We systematically studied correlations among important M6A methylation regulators, tumor mutational burden (TMB), and immune infiltration in clinical and sequencing data from the LUAD cohort of the cancer genome map (TCGA). The molecular subtype clusters 1 and 2 were identified by the consensus clustering of 16 M6A regulatory factors. Clinical prognosis, M6A regulatory factor expression, TMB, pathway enrichment, and immune cell infiltration significantly differed between clusters 1 and 2. Compared with other clinical traits, a prognostic risk score system constructed using the M6A regulatory factors HNRNPA2B1 and HNRNPC can serve as an independent prognostic method for LUAD, with higher predictive sensitivity and specificity. Risk scores were significantly higher for cluster 2 than 1, which was consistent with the trend towards a better prognosis in cluster 1. Overall, our findings revealed an important role of M6A methylation regulators in LUAD, and our risk scoring system involving these regulators might help to screen groups at high risk for LUAD and provide important theoretical bioinformatic support for evaluating the prognosis of such patients.