The oncogene MYC is dysregulated in a host of human cancers, and as an important point of convergence in multitudinous oncogenic signaling pathways, it plays a crucial role in tumor immune regulation in the tumor immune microenvironment (TIME). Specifically, MYC promotes the expression of immunosuppressive factors and inhibits the expression of immune activation regulators. Undoubtedly, a therapeutic strategy that targets MYC can initiate a new era of cancer treatment. In this review, we summarize the essential role of the MYC signaling pathway in tumor immunity and the development status of MYC-related therapies, including therapeutic strategies targeting MYC and combined MYC-based immunotherapy. These studies have reported extraordinary insights into the translational application of MYC in cancer treatment and are conducive to the emergence of more effective immunotherapies for cancer.

The efficacy of immunotherapy against colorectal cancer (CRC) is impaired by insufficient immune cell recruitment into the tumor microenvironment. Our study shows that targeting circDNA2v, a circular RNA commonly overexpressed in CRC, can be exploited to elicit cytotoxic T cell recruitment. circDNA2v functions through binding to IGF2BP3, preventing its ubiquitination, and prolonging the IGF2BP3 half-life, which in turn sustains mRNA levels of the protooncogene c-Myc. Targeting circDNA2v by gene silencing downregulates c-Myc to concordantly induce tumor cell senescence and the release of proinflammatory mediators. Production of CXCL10 and interleukin-9 by CRC cells is elicited through JAK-STAT1 signaling, in turn promoting the chemotactic and cytolytic activities of CD8⁺ T cells. Clinical evidence associates increased circDNA2v expression in CRC tissues with reductions in CD8⁺ T cell infiltration and worse outcomes. The regulatory relationship between circDNA2v, cellular senescence, and tumor-infiltrating lymphocytes thus provides a rational approach for improving immunotherapy in CRC.

Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with increasing global prevalence and is one of the leading causes of cancer-related mortality in the human population. Developing robust clinical prediction models and prognostic stratification strategies is crucial for developing individualized treatment plans. A range of novel forms of programmed cell death (PCD) plays a role in the pathological progression and advancement of HCC, and in-depth study of PCD is expected to further improve the prognosis of HCC patients. Sixteen patterns (apoptosis, autophagy, anoikis, lysosome-dependent cell death, immunogenic cell death, necroptosis, ferroptosis, netosis, pyroptosis, disulfidptosis, entotic cell death, cuproptosis, parthanatos, netotic cell death, alkaliptosis, and oxeiptosis) related to PCD were collected from the literatures and used for subsequent analysis. Supervised (Elastic net, Random Forest, XgBoost, and Boruta) and unsupervised (Nonnegative Matrix Factorization, NMF) clustering algorithms were applied to develop and validate a novel classifier for the individualized management of HCC patients at the transcriptomic, proteomic and single-cell levels. Multiple machine learning algorithms developed a programmed cell death index (PCDI) comprising five robust signatures (FTL, G6PD, SLC2A1, HTRA2, and DLAT) in four independent HCC cohorts, and a higher PCDI was predictive of higher pathological grades and worse prognoses. Furthermore, a higher PCDI was found to be correlated with the presence of a repressive tumor immune microenvironment (TME), as determined through an integrated examination of bulk and single-cell transcriptome data. In addition, patients with TP53 mutation had higher PCDI in comparison with TP53 WT patients. Three HCC subtypes were identified through unsupervised clustering (NMF), exhibiting distinct prognoses and significant biological processes, among the three subtypes, PCDcluster 3 was of particular interest as it contained a large proportion of patients with high risk and low metabolic activity. Construction and evaluation of the Nomogram model was drawn based on the multivariate logistic regression analysis, and highlighted the robustness of the Nomogram model in other independent HCC cohorts. Finally, to explore the prognostic value, we also validated the frequent upregulation of DLAT in a real-world cohort of human HCC specimens by qPCR, western blot, and immunohistochemical staining (IHC). Together, our work herein comprehensively emphasized PCD-related patterns and key regulators, such as DLAT, contributed to the evolution and prognosis of tumor foci in HCC patients, and strengthened our understanding of PCD characteristics and promoted more effective risk stratification strategies.

The underlying mechanisms between cancer stem cells (CSC) and epithelial-mesenchymal transition (EMT) in pancreatic cancer (PC) remain unclear. In this study, we identified TGIF2 as a target gene of CSC using sncRNA and machine learning. TGIF2 is closely related to the expression of SOX2, EGFR, and E-cadherin, indicating poor prognosis. Mechanistically, TGIF2 promoted the EMT phenotype and CSC properties following the activation of SOX2, Slug, CD44, and ERGF/MAPK signaling, which were rescued by SOX2 silencing. TGIF2 silencing contributes to the opposite phenotype via SOX2. Notably, Smad2 cooperates with TGIF2 to co-regulate the SOX2 promoter, which in turn promotes EMT and CSC signaling by transactivating Slug and EGFR, respectively. The transactivation of EGFR/MAPK signaling by SOX2 promotes TGIF2 nuclear translocation, forming a positive feedback loop in vitro. Moreover, the interaction of TGIF2 and SOX2 with EGFR inhibitors promoted subcutaneous tumors and liver metastasis in vivo. Thus, the TGIF2/SOX2 axis contributes to CSC, EMT, and chemoresistance, providing a promising target for PC therapy.