Background: The timing of surgery for patients with gastric cancer (GC) who undergo neoadjuvant chemotherapy (neoCT) was mainly guided by serial radiologic imaging. However, an earlier assessment was indispensable to avoid delayed treatment for nonresponders and excessive toxicity for responders. Our previous study has identified circulating extracellular vesicles-derived lncRNA-GC1 as a biomarker for early detection and monitoring progression of GC. However, the potential role of neoCT remains poorly understood. Methods: In this explorative biomarker analysis, we conducted a multi-cohort study to examine longitudinal levels of circulating extracellular vesicles-derived lncRNA-GC1 in 798 patients enrolled in the RESONANCE study (NCT01583361). Both circulating extracellular vesicles-derived lncRNA-GC1 and traditional gastrointestinal biomarkers were assessed at defined time nodes. Computed tomography (CT) scans were performed before treatment and 8–10 weeks and assessed based on the RECIST criteria. Results: Circulating extracellular vesicles-derived lncRNA-GC1 could be detected in 96.3% of patients at baseline, and significant reductions were observed before cycle 2 (P50%) and radiographic response (Cohen’s κ, 0.704). Importantly, circulating extracellular vesicles-derived lncRNA-GC1 maintained predictive value in two external cohorts. Patients with circulating extracellular vesicles-derived lncRNA-GC1 response showed superior disease-free survival [hazard ratio (HR), 0.6238; 95% CI, 0.4095–0.9501; P=0.0118] and overall survival (HR, 0.6131; 95% CI, 0.4016–0.9358; P=0.0090). Conclusion: Circulating extracellular vesicles-derived lncRNA-GC1 is an early marker of neoCT efficacy and predicts superior survival in GC patients treated with neoCT.

Efforts to predict the outcomes of patients with gastric cancer (GC) following immune checkpoint inhibitor (ICI) treatments remain limited, owing to a lack of reliable biomarkers. Studies have found that extracellular vesicle (EV)-derived lncRNA-GC1 may serve as a GC-specific biomarker. This study was designed to expand on these previous results by estimating the usefulness of EV-derived lncRNA-GC1 as a predictive indicator for patients with GC who undergo ICI treatments. EV-derived lncRNA-GC1 levels were measured using real-time polymerase chain reaction (RT-PCR) in patients with unresectable or metastatic GC who were receiving ICI treatments. Correlations between this biomarker and ICI treatment outcomes were analyzed in a training cohort (n = 136), two external validation cohorts (n = 188 and n = 214), one expanding cohort (n = 30), and one prospective cohort (n = 192). Circulating EVs exhibited a lncRNA-GC1 expression profile that was distinct from that of tissues or circulating cells. EV-derived lncRNA-GC1 levels were found to be independent of PD-L1 expression status or the density of CD8+ T cell infiltration. EV-derived lncRNA-GC1 could be used to effectively predict ICI-related patient outcomes, and could be used for dynamic monitoring throughout treatments. Lower levels of EV-derived lncRNA-GC1 were associated with tumor microenvironmental characteristics such as more robust antitumor immunity—including higher levels of activated CD8+ T/NK cells and an increased TH1/TH2 ratio. Such biomarkers can be stably detected in clinical practice. These results were consistent in both the two external validation cohorts and the one prospective cohort. EV-derived lncRNA-GC1 can be used to reliably predict immunotherapeutic outcomes in patients with GC who undergo ICI treatments, suggesting that targeted analyses of this lncRNA may be useful for guiding treatment planning, monitoring, and associated decision-making processes.

Resistance to poly adenosine diphosphate (ADP)-ribose polymerase inhibitor (PARPi) presents a considerable obstacle in the treatment of ovarian cancer. F-box and tryptophan-aspartic (WD) repeat domain containing 11 (FBXW11) modulates the ubiquitination of growth-and invasion-related factors in lung cancer, colorectal cancer, and osteosarcoma. The function of FBXW11 in PARPi therapy is still ambiguous. In this study, RNA sequencing (RNA-seq) showed that FBXW11 expression was raised in ovarian cancer cells that had been treated with PARPi. FBXW11 was abnormally expressed at low levels in high-grade serous ovarian cancer (HGSOC) tissues, and low levels of FBXW11 were associated with shorter overall survival (OS) and progression-free survival (PFS) in HGSOC patients. Overexpressing FBXW11 made ovarian cancer more sensitive to PARPi, while knocking down FBXW11 made it less sensitive. The four-dimensional (4D) label-free quantitative proteomic analysis revealed that FBXW11 targeted S100 calcium binding protein A11 (S100A11) and promoted its degradation through ubiquitination. The increased degradation of S100A11 led to less efficient DNA damage repair, which in turn contributed to increased PARPi-induced DNA damage. The role of FBXW11 in promoting PARPi sensitivity was also confirmed in xenograft mouse models. In summary, our study confirms that FBXW11 promotes the susceptibility of ovarian cancer cells to PARPi via affecting S100A11-mediated DNA damage repair.

Metabolism-associated genes (MAGs) are important regulators of tumor progression and can affect a variety of physiological processes. In this study, we focused on the relationship between MAGs and Ovarian cancer (OC) prognosis. Metabolism-related genes were extracted from the Cancer Genome Atlas (TCGA) database. Through univariate COX and lasso regression models, a dynamic risk model based on MAGs was established. Compared with other clinical factors, demonstrated the ability of the model to predict the prognosis of patients with OC. The clinical samples were used to verify the expression of these MAGs. A metabolism-associated gene signature was constructed by LASSO Cox regression analysis in OC, which was composed of 3-MAGs (PTGIS, AOC3, and IDO1). The signature was used to classify the OC patients into high-risk and low-risk groups. The overall survival of the low-risk group was significantly better than that of the high-risk group. The analysis of the therapeutic effect of bevacizumab showed that bevacizumab was not conducive to improving the prognosis of the low-risk group. We constructed a prognostic model of MAGs in OC, which can be used to predict the prognosis of OC patients and may have a good guiding significance in the individualized treatment of patients.