The high morbidity and mortality of hepatocellular carcinoma (HCC) impose a substantial economic burden on patients’ families and society, and the majority of HCC patients are detected at advanced stages and experience poor therapeutic outcomes, whereas early-stage patients exhibit the most favorable prognosis following radical treatment. In this study, we utilized a computational framework to integrate multi-omics data from HCC patients using the latest 10 different clustering algorithms, which were then employed a diverse set of 101 combinations derived from 10 different machine learning algorithms to develop a consensus machine learning-based signature (CMLBS). Using multi-omics consensus clustering, we distinguished two cancer subtypes (CSs) of HCC, and found that CS2 patients exhibited superior overall survival (OS) outcomes. In TCGA-LIHC, ICGC-LIRI, and multiple immunotherapy cohorts, low-CMLBS patients demonstrated favorable clinical outcomes and enhanced responsiveness to immunotherapy. Encouragingly, we observed that the high-CMLBS patients may exhibit increased sensitivity to Alpelisib, AZD7762, BMS-536,924, Carmustine, and GDC0810, whereas they may demonstrate reduced sensitivity to Axitinib, AZD6482, AZD8055, Entospletinib, GSK269962A, GSK1904529A, and GSK2606414, suggesting that CMLBS may contribute to the selection of chemotherapeutic agents for HCC patients. Therefore, in-depth examination of data from multi-omics data can provide valuable insights and contribute to the refinement of the molecular classification of HCC. In addition, the CMLBS model demonstrates potential as a screening tool for identifying HCC patients who may derive benefit from immunotherapy, and it possesses practical utility in the clinical management of HCC.

Lung cancer remains the most lethal cancer worldwide because of its high metastasis potential. Epithelial-mesenchymal transition (EMT) is known as the first step of the metastasis cascade, but the potential regulatory mechanisms of EMT have not been clearly established. In this study, we first found that low CUEDC1 expression correlated with lymph node metastasis in non-small cell lung cancer (NSCLC) patients using immunohistochemistry (IHC). CUEDC1 knockdown promoted the metastasis of NSCLC cells and EMT process and activated TβRI/Smad signaling pathway. Overexpression of CUEDC1 decreased the metastatic potential of lung cancer cells and inhibited the EMT process and inactivated TβRI/Smad signaling pathway. Immunoprecipitation (IP) assays showed that Smurf2 is a novel CUEDC1-interacting protein. Furthermore, CUEDC1 could regulate Smurf2 expression through the degradation of Smurf2. Overexpression of Smurf2 abolished CUEDC1 knockdown induced-EMT and the activation of TβRI/Smad signaling pathway, while siRNA Smurf2 reversed CUEDC1 overexpression-mediated regulation of EMT and TβRI/Smad signaling pathway. Additionally, CUEDC1 inhibited proliferation and promoted apoptosis of NSCLC cells. In vivo, CUEDC1-knockdown cells promoted metastasis and tumor growth compared with control cells. In conclusion, our findings indicate that the crucial role of CUEDC1 in NSCLC progression and provide support for its clinical investigation for therapeutic approaches.

Human induced pluripotent stem cells (iPSCs) are important source for regenerative medicine. However, the links between pluripotency and oncogenic transformation raise safety issues. To understand the characteristics of iPSC-derived cells at single-cell resolution, we directly reprogrammed two human iPSC lines into cardiomyocytes and collected cells from four time points during cardiac differentiation for single-cell sequencing. We captured 32,365 cells and identified five molecularly distinct clusters that aligned well with our reconstructed differentiation trajectory. We discovered a set of dynamic expression events related to the upregulation of oncogenes and the decreasing expression of tumor suppressor genes during cardiac differentiation, which were similar to the gain-of-function and loss-of-function patterns during oncogenesis. In practice, we characterized the dynamic expression of the TP53 and Yamanaka factor genes (OCT4, SOX2, KLF4 and MYC), which were widely used for human iPSCs lines generation; and revealed the co-occurrence of MYC overexpression and TP53 silencing in some of human iPSC-derived TNNT2+ cardiomyocytes. In summary, our oncogenic expression atlas is valuable for human iPSCs application and the single-cell resolution highlights the clues potentially associated with the carcinogenic risk of human iPSC-derived cells.