The incidence and mortality from malignant tumors continue to rise each year. Consequently, early diagnosis and intervention are vital for improving patient’ prognosis and survival. The traditional pathological tissue biopsy is currently considered the gold standard for cancer diagnosis. However, it suffers from several limitations including invasiveness, sometimes not repeatable or unsuitable, and the inability to capture the dynamic nature of tumors in terms of space and time. Consequently, these limit the application of tissue biopsies for the diagnosis of early-stage tumors and have redirected the research focus towards liquid biopsies. Blood-based liquid biopsies have thus emerged as a promising option for non-invasive assessment of tumor-specific biomarkers. These minimally invasive, easily accessible, and reproducible tests offer several advantages, such as being mostly complication-free and efficient at monitoring tumor progression and tracing drug resistance. Liquid biopsies show great potential for cancer prediction, diagnosis, and prognostic assessment. Circulating tumor-educated platelets (TEPs) possess the unique ability to absorb nucleic acids from the bloodstream and to modify transcripts derived from megakaryocytes in response to external signals. In addition, circulating free RNA (cfRNA) constitutes a significant portion of the biomolecules present in the bloodstream. This paper aims to provide a comprehensive overview of the current research status regarding TEP RNA and cfRNA in liquid biopsies from various tumor types. Our analysis includes cancers of the lung, liver, pancreas, breast, nasopharynx, ovary and colon, as well as multiple myeloma and sarcoma. By synthesizing this information, we intend to establish a solid theoretical foundation for exploring potential applications of circulating RNA as a reliable biomarker for tumor diagnosis and monitoring.

Gastric cancer (GC) is the third leading cause of cancer death in China and the outcome of GC patients is poor. The aim of the research is to study the prognostic factors of gastric cancer patients who had curative intent or palliative resection, completed clinical database and follow-up.

Background: The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC). Methods: GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed. Results: A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS. Conclusion: The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

Liver cancer stem-like cells (LCSCs) are the main cause of heterogeneity and poor prognosis in hepatocellular carcinoma (HCC). In this study, we aimed to explore the origin of LCSCs and the role of the TOP2A/β-catenin/YAP1 axis in tumor stemness and progression. Using single-cell RNA-seq analysis, we identified TOP2A⁺CENPF⁺ LCSCs, which were mainly regulated by CD168⁺ M2-like macrophages. Furthermore, spatial location analysis and fluorescent staining confirmed that LCSCs were enriched at tumor margins, constituting the spatial heterogeneity of HCC. Mechanistically, TOP2A competitively binds to β-catenin, leading to disassociation of β-catenin from YAP1, promoting HCC stemness and overgrowth. Our study provides valuable insights into the spatial transcriptome heterogeneity of the HCC microenvironment and the critical role of TOP2A/β-catenin/YAP1 axis in HCC stemness and progression.