M6A-mediated upregulation of lncRNA TUG1 in liver cancer cells regulates the antitumor response of CD8+ T cells and phagocytosis of macrophages.

Tumor immune evasion relies on the crosstalk between tumor cells and adaptive/innate immune cells. Immune checkpoints play critical roles in the crosstalk, and immune checkpoint inhibitors have achieved promising clinical effects. The long non-coding RNA taurine-upregulated gene 1 (TUG1) is upregulated in hepatocellular carcinoma (HCC). However, how TUG1 is upregulated and the effects on tumor immune evasion are incompletely understood. Here, METTL3-mediated m6A modification led to TUG1 upregulation is demonstrated. Knockdown of TUG1 inhibited tumor growth and metastasis, increased the infiltration of CD8+ T cells and M1-like macrophages in tumors, promoted the activation of CD8+ T cells through PD-L1, and improved the phagocytosis of macrophages through CD47. Mechanistically, TUG1 regulated PD-L1 and CD47 expressions by acting as a sponge of miR-141 and miR-340, respectively. Meanwhile, TUG1 interacted with YBX1 to facilitate the upregulation of PD-L1 and CD47 transcriptionally, which ultimately regulated tumor immune evasion. Clinically, TUG1 positively correlated with PD-L1 and CD47 in HCC tissues. Moreover, the combination of Tug1-siRNA therapy with a Pdl1 antibody effectively suppressed tumor growth. Therefore, the mechanism of TUG1 in regulating tumor immune evasion is revealed and can inform existing strategies targeting TUG1 for enhancing HCC immune therapy and drug development.

Advances in the molecular regulation mechanism of tumor dormancy and its therapeutic strategy.

Tumor dormancy is a stage in the growth and development of malignant cells and is one of the biological characteristics of malignant cells. Complex transitions involving dormant tumor cells between quiescent and proliferative states pose challenges for tumor eradication. This paper explores the biological features and molecular mechanisms of tumor dormancy and highlights emerging therapies. The strategies discussed promise innovative clinical potential against malignant tumors. Understanding the mechanisms of dormancy can help provide valuable insights into the diagnosis and treatment of malignant tumors to advance the fight against this world problem.

Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning.

Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-ß signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-ß-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy.

Cancer-associated fibroblasts contribute to the immunosuppressive landscape and influence the efficacy of the combination therapy of PD-1 inhibitors and antiangiogenic agents in hepatocellular carcinoma.

BACKGROUND: Chronic inflammation is considered the most critical predisposing factor of hepatocellular carcinoma (HCC), with inflammatory cell heterogeneity, hepatic fibrosis accumulation, and abnormal vascular proliferation as prominent features of the HCC tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) play a key role in HCC TME remodeling. Therefore, the level of abundance of CAFs may significantly affect the prognosis and outcome in HCC patients. METHODS: Unsupervised clustering was performed based on 39 genes related to CAFs in HCC identified by single-cell RNA sequencing data. Patients of bulk RNA were grouped into CAF low abundance cluster and high abundance clusters. Subsequently, prognosis, immune infiltration landscape, metabolism, and treatment response between the two clusters were investigated and validated by immunohistochemistry. RESULTS: Patients in the CAF high cluster had a higher level of inflammatory cell infiltration, a more significant immunosuppressive microenvironment, and a significantly worse prognosis than those in the low cluster. At the metabolic level, the CAF high cluster had lower levels of aerobic oxidation and higher angiogenic scores. Drug treatment response prediction indicated that the CAF high cluster could have a better response to PD-1 inhibitors and conventional chemotherapeutic agents for HCC such as anti-angiogenic drugs, whereas CAF low cluster may be more sensitive to transarterial chemoembolization treatment. CONCLUSIONS: This study not only revealed the TME characteristics of HCC with the difference in CAF abundance but also further confirmed that the combination therapy of PD-1 inhibitors and anti-angiogenic drugs may be more valuable for patients with high CAF abundance.