STING, an endoplasmic reticulum (ER) transmembrane protein, mediates innate immune activation upon cGAMP stimulation and is degraded through autophagy. Here, we report that activated STING could be transferred between cells to promote antitumor immunity, a process triggered by RAB22A-mediated non-canonical autophagy. Mechanistically, RAB22A engages PI4K2A to generate PI4P that recruits the Atg12–Atg5–Atg16L1 complex, inducing the formation of ER-derived RAB22A-mediated non-canonical autophagosome, in which STING activated by agonists or chemoradiotherapy is packaged. This RAB22A-induced autophagosome fuses with RAB22A-positive early endosome, generating a new organelle that we name Rafeesome (RAB22A-mediated non-canonical autophagosome fused with early endosome). Meanwhile, RAB22A inactivates RAB7 to suppress the fusion of Rafeesome with lysosome, thereby enabling the secretion of the inner vesicle of the autophagosome bearing activated STING as a new type of extracellular vesicle that we define as R-EV (RAB22A-induced extracellular vesicle). Activated STING-containing R-EVs induce IFNβ release from recipient cells to the tumor microenvironment, promoting antitumor immunity. Consistently, RAB22A enhances the antitumor effect of the STING agonist diABZI in mice, and a high RAB22A level predicts good survival in nasopharyngeal cancer patients treated with chemoradiotherapy. Our findings reveal that Rafeesome regulates the intercellular transfer of activated STING to trigger and spread antitumor immunity, and that the inner vesicle of non-canonical autophagosome originated from ER is secreted as R-EV, providing a new perspective for understanding the intercellular communication of organelle membrane proteins.

To investigate the value of preoperative systemic inflammation response (SIRS) score in predicting the prognosis of hepatocellular carcinoma (HCC) after hepatectomy. Patients and Methods:The study analyzed 1001 patients with pathologically proven HCC who received curative resection at Sun Yat-sen University Cancer Center between March 2016 and May 2020. Patients were randomly divided into a training cohort (n = 751) and a validation cohort (n = 250). Clinicopathological characteristics were collected retrospectively. The SIRS score formula was based on the results of a multivariate cox analysis of hematological inflammation indexes in the training cohort. Then, a nomogram consisting of the SIRS score was constructed and the calibration plot, areas under the receiver operating characteristic (AUC) curve, and decision curve analysis (DCA) showed good predictive ability. Results: Univariate and multivariate cox analysis revealed that the SIRS score is an independent prognostic factor for OS in HCC patients. A higher SIRS score was associated with a larger maximum lesion diameter, poor tumor differentiation, a greater possibility of vascular invasion, and a more advanced cancer stage. When the nomogram was used to predict 1-year, 3-year, and 5-year survival rates, the AUC in the training cohort was 0.763, 0.712, and 0.687, respectively; In the validation cohort, it was 0.715, 0.648, and 0.614, respectively. The AUC of this nomogram showed significantly better predictive performance than those of commonly used staging systems. Conclusion:The preoperative SIRS score has good efficacy in predicting the prognosis of HCC patients undergoing hepatectomy, and nomograms based on the SIRS score can potentially guide individualized follow-up and adjuvant therapy.

Objective To develop a risk stratification model based on the International Federation of Gynecology and Obstetrics (FIGO) staging combined with squamous cell carcinoma antigen (SCC-Ag) for the classification of patients with cervical squamous cell carcinoma (CSCC) into different risk groups. Methods We retrospectively reviewed the data of 664 women with stage IIA–IVB CSCC according to the 2018 FIGO staging system who received definitive radiotherapy from March 2013 to December 2017 at the department of radiation oncology of Sun Yat-sen University Cancer Center. Cutoff values for continuous variables were estimated using receiver operating characteristic curve analysis. Using recursive partitioning analysis (RPA) modeling, overall survival was predicted based on the prognostic factors determined via Cox regression analysis. The predictive performance of the RPA model was assessed using the consistency index (C-index). Intergroup survival differences were determined and compared using Kaplan–Meier analysis and the log-rank test. Results Multivariate Cox regression analysis identified post-treatment SCC-Ag (< 1.35 ng/mL and > 1.35 ng/mL; hazard ratio (HR), 4.000; 95% confidence interval (CI), 2.911–5.496; P < 0.0001) and FIGO stage (II, III, and IV; HR, 2.582, 95% CI, 1.947–3.426; P < 0.0001) as the independent outcome predictors for overall survival. The RPA model based on the above prognostic factors divided the patients into high-, intermediate-, and low-risk groups. Significant differences in overall survival were observed among the three groups (5-year overall survival: low vs. intermediate vs. high, 91.3% vs. 76.7% vs. 29.5%, P < 0.0001). The predictive performance of the RPA model (C-index, 0.732; 95% CI, 0.701–0.763) was prominently superior to that of post-treatment SCC-Ag (C-index, 0.668; 95% CI, 0.635–0.702; P < 0.0001) and FIGO stage (C-index, 0.663; 95% CI, 0.631–0.695; P < 0.0001). Conclusions The RPA model based on FIGO staging and post-treatment SCC-Ag can predict the overall survival of patients with CSCC, thereby providing a guide for the formulation of risk-adaptive treatment and individualized follow-up strategies.

Objectives: To investigate the effect of chemotherapy cycles on survival outcomes in small cell neuroendocrine carcinoma of cervix (SCNEC). Methods: Clinical records of 103 biopsy-proven SCNEC were identified from Sun Yat-sen University Cancer Center. The cycles-dependent effect of chemotherapy on survival was estimated by restricted cubic splines (RCS) based cox regression model. Results: Through RCS analysis, we observed an inverse correlation between chemotherapy cycles and progression/death; the risks (hazard ratio [HR]) of progression/death decreased sharply until 5 cycles of chemotherapy. Long-course chemotherapy (≥5 cycles) was associated with significantly superior PFS (≥5 vs 1-4: median PFS, 58.6 months vs 25.4 months, P =0.027) and prolonged OS (≥5 vs 1-4: median OS, 65.1 months vs 37.7 months, P =0.168) than short-course chemotherapy (1-4 cycles). Subgroup analyses suggested that chemotherapy courses had significant interaction with FIGO stage; the survival benefit of long-course chemotherapy was identified in FIGO IIB-IIIC (HRPFS 0.41, 95% CI 0.18-0.92; HROS 0.41, 95% CI 0.17-0.95), rather than FIGO I-IIA (HRPFS 0.67, 95% CI 0.34-1.34; HROS 0.88, 95% CI 0.40-1.97). Additionally, chemotherapy regimen was observed to be relevant to survival outcomes; EP regimen demonstrated obvious prolonged PFS (median PFS: EP vs non-EP, 44.7 months vs 18.0 months) and OS (median OS: EP vs non-EP, 63.3 months vs 41.0 months) than those treated with non-EP regimen. Conclusion: Chemotherapy with ≥5 cycles significantly improved PFS and OS in FIGO stage IIB-IIIC SCNEC, whereas a short course of <5 cycles was adequate for FIGO I-IIA disease.

Objective To develop a risk stratification model based on the International Federation of Gynecology and Obstetrics (FIGO) staging combined with squamous cell carcinoma antigen (SCC-Ag) for the classification of patients with cervical squamous cell carcinoma (CSCC) into different risk groups. Methods We retrospectively reviewed the data of 664 women with stage IIA–IVB CSCC according to the 2018 FIGO staging system who received definitive radiotherapy from March 2013 to December 2017 at the department of radiation oncology of Sun Yat-sen University Cancer Center. Cutoff values for continuous variables were estimated using receiver operating characteristic curve analysis. Using recursive partitioning analysis (RPA) modeling, overall survival was predicted based on the prognostic factors determined via Cox regression analysis. The predictive performance of the RPA model was assessed using the consistency index (C-index). Intergroup survival differences were determined and compared using Kaplan–Meier analysis and the log-rank test. Results Multivariate Cox regression analysis identified post-treatment SCC-Ag (< 1.35 ng/mL and > 1.35 ng/mL; hazard ratio (HR), 4.000; 95% confidence interval (CI), 2.911–5.496; P < 0.0001) and FIGO stage (II, III, and IV; HR, 2.582, 95% CI, 1.947–3.426; P < 0.0001) as the independent outcome predictors for overall survival. The RPA model based on the above prognostic factors divided the patients into high-, intermediate-, and low-risk groups. Significant differences in overall survival were observed among the three groups (5-year overall survival: low vs. intermediate vs. high, 91.3% vs. 76.7% vs. 29.5%, P < 0.0001). The predictive performance of the RPA model (C-index, 0.732; 95% CI, 0.701–0.763) was prominently superior to that of post-treatment SCC-Ag (C-index, 0.668; 95% CI, 0.635–0.702; P < 0.0001) and FIGO stage (C-index, 0.663; 95% CI, 0.631–0.695; P < 0.0001). Conclusions The RPA model based on FIGO staging and post-treatment SCC-Ag can predict the overall survival of patients with CSCC, thereby providing a guide for the formulation of risk-adaptive treatment and individualized follow-up strategies.