Ovarian cancer is the leading cause of death in gynecological cancer. Cancer stem cells (CSCs) contribute to the occurrence, progression and resistance. Small nucleolar RNAs (SnoRNAs), a class of small molecule non-coding RNA, involve in the cancer cell stemness and tumorigenesis. In this study, we screened out SNORNAs related to ovarian patient’s prognosis by analyzing the data of 379 cases of ovarian cancer patients in the TCGA database, and analyzed the difference of SNORNAs expression between OVCAR-3 (OV) sphere-forming (OS) cells and OV cells. After overexpression or knockdown SNORD89, the expression of Nanog, CD44, and CD133 was measured by qRT-PCR or flow cytometry analysis in OV, CAOV-3 (CA) and OS cells, respectively. CCK-8 assays, plate clone formation assay and soft agar colony formation assay were carried out to evaluate the changes of cell proliferation and self-renewal ability. Scratch migration assay and trans-well invasion analysis were used for assessing the changes of migration and invasion ability. High expression of SNORD89 indicates the poor prognosis of ovarian cancer patients and was associated with patients’ age, therapy outcome. SNORD89 highly expressed in ovarian cancer stem cells. The overexpression of SNORD89 resulted in the increased stemness markers, S phase cell cycle, cell proliferation, invasion and migration ability in OV and CA cells. Conversely, these phenomena were reversed after SNORD89 silencing in OS cells. Further, we found that SNORD89 could upregulate c-Myc and Notch1 expression in mRNA and protein levels. SNORD89 deteriorates the prognosis of ovarian cancer patients by regulating Notch1-c-Myc pathway to promote cell stemness and acts as an oncogene in ovarian tumorigenesis. Consequently, SNORD89 can be a novel prognostic biomarker and therapeutic target for ovarian cancer.

Necroptosis is a kind of programmed necrosis, which is different from apoptosis and pyroptosis. Its molecular mechanism has been described in inflammatory diseases. Gastric cancer (GC) is one of the most common malignancies worldwide with the third highest mortality. However, the role of necroptosis in the occurrence and progression of GC remains largely unexplored. Therefore, we investigated necroptosis-related genes (NRGs) by analyzing public transcriptomic data from GC samples. Our results indicate that 83 of 740 NRGs are dysregulated in GC tissues. Next, we identified necroptosis-associated early diagnosis and prognostic gene signatures for GC using machine learning. 2-NRGs (CCT6A and FAP) and 4-NRGs (ZFP36, TP53I3, FAP, and CCT6A), respectively, can effectively assess the risk of early GC (AUC = 0.943) and the prognosis of GC patients (AUC = 0.866). Through in-depth analysis, we were pleasantly surprised to find that there was a significant correlation between the 4-NRGs and GC immunotherapy effect and immune checkpoint inhibitors (ICIs), which could be used for the evaluation of immunosuppressants. Finally, we identified the core gene FAP, and established the relationship between FAP and ICIs in GC. These findings could provide a new target for immunotherapy for GC and a more effective treatment scheme for GC patients.

Necroptosis is a type of programmed necrosis that is different from apoptosis and necrosis. Lung cancer has the highest incidence and mortality worldwide, and lung adenocarcinoma is the most common subtype of lung cancer. However, the role of necroptosis in the occurrence and development of LUAD remains largely unexplored. In this paper, four NRGs and nine NRGs determined by big data analysis were used to effectively predict the risk of early LUAD (AUC = 0.994) and evaluate the prognostic effect on LUAD patients (AUC = 0.826). Meanwhile, ESTIMATE, single-sample gene set enrichment analysis (ssGSEA), genomic variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune checkpoint analysis were used to explore the enrichment characteristics and immune research related to the prognostic model. In deep data mining, we were surprised to find that prognostic models also regulate the immune microenvironment, cell cycle, and DNA damage repair mechanisms. Thus, we demonstrated a significant correlation between model evaluation results, ICI treatment, and chemotherapeutic drug sensitivity. The low-risk population has a stronger tumor immune response, and the potential for ICI treatment is greater. People at high risk respond less to immunotherapy but respond well to chemotherapy drugs. In addition, PANX1, a core gene with important value in immune regulation, prognosis assessment, and early diagnosis, has been identified for the first time, which provides a new target for the immunotherapy of LUAD as well as a new theoretical basis for the basic research, clinical diagnosis, and individualized treatment of LUAD.

Preoperative T staging of gastric cancer is critical for therapeutic stratification, yet conventional contrast-enhanced CT interpretation shows subjectivity and inconsistent reliability. We developed GTRNet, an interpretable end-to-end deep-learning framework that classifies T1–T4 from routine CT without manual segmentation or annotation. In a retrospective multicenter study of 1792 patients, CT images underwent standardized preprocessing and the largest axial tumor slice was used for training; performance was then tested in two independent external cohorts. GTRNet achieved high discrimination (AUC 0.86–0.95) and accuracy (81–85%) in internal and external tests, surpassing radiologists. Grad-CAM heatmaps localized attention to the gastric wall and serosa. Combining a deep-learning rad-score with tumor size, differentiation and Lauren subtype, we constructed a nomogram with good calibration and higher net clinical benefit than conventional approaches. This automated and interpretable pipeline may standardize CT-based staging and support preoperative decision-making and neoadjuvant-therapy selection.