Objective To explore the biological characteristics associated with different subtypes and the response to immunotherapy by integrating multiomics analysis and artificial neural networks(ANN)to delineate the precise molecular subtypes of adrenocortical carcinoma(ACC)and establish a prognostic prediction model,in order to provide reference for the accurate prognosis assessment and individualized treatment of ACC.Methods The multiomics data of 44 Chinese ACC patients admitted to the Department of Urology,West China Hospital of Sichuan University during Jan.1,2012 and Dec.31,2022 were integrated,including genomic,transcriptomic and clinical features.Ten different clustering algorithms were employed for consensus clustering to identify robust molecular subtypes.The results were then incorporated into an ANN model to construct an ANN-driven prognostic index(ANPI)for patient stratification and survival prediction.Results Three distinct molecular subtypes(cancer subtypes,CS1-3)with significantly different prognoses were identified,among which CS1 exhibited the poorest survival outcomes.A set of 20 core genes was selected to form the basis of the ANPI model.ANPI effectively stratified patients into high-and low-risk groups:patients in the low-ANPI group had significantly better overall survival and exhibited"hot tumor"immune phenotypes,suggesting greater benefits from immunotherapy.In contrast,high-ANPI patients had worse prognoses and displayed"cold tumor"characteristics with weaker immunotherapy responses.Conclusion Our integrative multiomics analysis illustrated the molecular landscape of ACC in the Chinese population and uncovered the key immune-related features linked to clinical outcomes.The ANPI model demonstrated strong performance in prognostic prediction and immunotherapy response assessment,offering a valuable tool for precision oncology and clinical decision-making.

Objective:To investigate the value of 24 h urinary aldosterone(24 h-UAC) measurement by liquid chromatography-tandem mass spectrometry(LC-MS/MS) in the subtype classification of primary aldosteronism(PA).Methods:A total of 86 patients with PA, including 51 with unilateral primary aldosteronism(UPA) and 35 with bilateral primary aldosteronism(BPA), were enrolled in the Department of Endocrinology and Metabolism at West China Hospital between January 2018 and December 2022. Plasma aldosterone concentration(PAC), plasma renin concentration(PRC) and 24 h-UAC were measured by LC-MS/MS. 24-hour urinary electrolytes and 24-hour urinary creatinine(24 h-UCR) were also measured. The diagnostic value of 24 h-UAC in PA subtype classification was evaluated using receiver operating characteristic(ROC) curve analysis. Multivariate logistic regression analysis was conducted with PA subtypes as the dependent variable(UPA=1, BPA=0) to establish a diagnostic model for differentiating unilateral from bilateral lesions, and its performance was compared with published Chinese classification models. Results:There were no statistical differences between the UPA and BPA groups in terms of age, gender, BMI, systolic and diastolic blood pressure, 24 h urinary potassium, sodium, chloride, 24 h-UCR and PRC( P<0.05). The lowest plasma potassium level was significantly lower in the UPA group than in the BPA group, while PAC, 24 h-UAC, aldosterone-renin ratio(ARR), and 24 h-UAC/UCR were significantly higher( P<0.05). The detection rate of typical adenomas on imaging also showed a significant difference between the two groups( P<0.05). The area under the ROC curve(AUC) of 24 h-UAC for differentiating UPA from BPA was 0.829(95% CI 0.733-0.902), with an optimal cut-off value of 15.4 μg/24 h, yielding a sensitivity of 68.63% and a specificity of 88.57%( P<0.001). At a cut-off value of 24.5 μg/24 h, specificity reached 100%, with a sensitivity of 27.45%. Multivariate analysis indicated that a combined model incorporating 24 h-UAC, the lowest plasma potassium level, and imaging findings of typical adenomas significantly improved diagnostic accuracy for PA subtyping, achieving a specificity of 91.43%. Compared with the existing Chinese modified Küpers scoring model and CONPASS prediction model, this model demonstrated higher diagnostic efficiency, a lower missed diagnosis rate, and a misdiagnosis rate intermediate between the two. Conclusion:The 24 h-UAC in UPA patients is significantly higher than in BPA patients, making it a valuable marker for PA subtype classification. A predictive model combining 24 h-UAC, the lowest plasma potassium level, and imaging evidence of typical adenomas demonstrated high diagnostic accuracy for PA subtype classification and may provide valuable guidance for clinical decision-making.

Objective To explore the biological characteristics associated with different subtypes and the response to immunotherapy by integrating multiomics analysis and artificial neural networks(ANN)to delineate the precise molecular subtypes of adrenocortical carcinoma(ACC)and establish a prognostic prediction model,in order to provide reference for the accurate prognosis assessment and individualized treatment of ACC.Methods The multiomics data of 44 Chinese ACC patients admitted to the Department of Urology,West China Hospital of Sichuan University during Jan.1,2012 and Dec.31,2022 were integrated,including genomic,transcriptomic and clinical features.Ten different clustering algorithms were employed for consensus clustering to identify robust molecular subtypes.The results were then incorporated into an ANN model to construct an ANN-driven prognostic index(ANPI)for patient stratification and survival prediction.Results Three distinct molecular subtypes(cancer subtypes,CS1-3)with significantly different prognoses were identified,among which CS1 exhibited the poorest survival outcomes.A set of 20 core genes was selected to form the basis of the ANPI model.ANPI effectively stratified patients into high-and low-risk groups:patients in the low-ANPI group had significantly better overall survival and exhibited"hot tumor"immune phenotypes,suggesting greater benefits from immunotherapy.In contrast,high-ANPI patients had worse prognoses and displayed"cold tumor"characteristics with weaker immunotherapy responses.Conclusion Our integrative multiomics analysis illustrated the molecular landscape of ACC in the Chinese population and uncovered the key immune-related features linked to clinical outcomes.The ANPI model demonstrated strong performance in prognostic prediction and immunotherapy response assessment,offering a valuable tool for precision oncology and clinical decision-making.

Objective:To investigate the value of 24 h urinary aldosterone(24 h-UAC) measurement by liquid chromatography-tandem mass spectrometry(LC-MS/MS) in the subtype classification of primary aldosteronism(PA).Methods:A total of 86 patients with PA, including 51 with unilateral primary aldosteronism(UPA) and 35 with bilateral primary aldosteronism(BPA), were enrolled in the Department of Endocrinology and Metabolism at West China Hospital between January 2018 and December 2022. Plasma aldosterone concentration(PAC), plasma renin concentration(PRC) and 24 h-UAC were measured by LC-MS/MS. 24-hour urinary electrolytes and 24-hour urinary creatinine(24 h-UCR) were also measured. The diagnostic value of 24 h-UAC in PA subtype classification was evaluated using receiver operating characteristic(ROC) curve analysis. Multivariate logistic regression analysis was conducted with PA subtypes as the dependent variable(UPA=1, BPA=0) to establish a diagnostic model for differentiating unilateral from bilateral lesions, and its performance was compared with published Chinese classification models. Results:There were no statistical differences between the UPA and BPA groups in terms of age, gender, BMI, systolic and diastolic blood pressure, 24 h urinary potassium, sodium, chloride, 24 h-UCR and PRC( P<0.05). The lowest plasma potassium level was significantly lower in the UPA group than in the BPA group, while PAC, 24 h-UAC, aldosterone-renin ratio(ARR), and 24 h-UAC/UCR were significantly higher( P<0.05). The detection rate of typical adenomas on imaging also showed a significant difference between the two groups( P<0.05). The area under the ROC curve(AUC) of 24 h-UAC for differentiating UPA from BPA was 0.829(95% CI 0.733-0.902), with an optimal cut-off value of 15.4 μg/24 h, yielding a sensitivity of 68.63% and a specificity of 88.57%( P<0.001). At a cut-off value of 24.5 μg/24 h, specificity reached 100%, with a sensitivity of 27.45%. Multivariate analysis indicated that a combined model incorporating 24 h-UAC, the lowest plasma potassium level, and imaging findings of typical adenomas significantly improved diagnostic accuracy for PA subtyping, achieving a specificity of 91.43%. Compared with the existing Chinese modified Küpers scoring model and CONPASS prediction model, this model demonstrated higher diagnostic efficiency, a lower missed diagnosis rate, and a misdiagnosis rate intermediate between the two. Conclusion:The 24 h-UAC in UPA patients is significantly higher than in BPA patients, making it a valuable marker for PA subtype classification. A predictive model combining 24 h-UAC, the lowest plasma potassium level, and imaging evidence of typical adenomas demonstrated high diagnostic accuracy for PA subtype classification and may provide valuable guidance for clinical decision-making.

Objective:To compare the efficacy and safety of retroperitoneal laparoscopic adrenalectomy (RLA) and transperitoneal laparoscopic adrenalectomy (TLA) in the treatment of localized adrenocortical carcinoma (ACC).Methods:The data of 22 patients with stage Ⅰ/Ⅱ ACC underwent laparoscopic adrenalectomy in our institution from January 2009 to December 2018 were retrospectively analyzed. According to the different surgical approaches, these patients were divided into RLA and TLA groups. Eleven patients underwent RLA and 11 patients underwent TLA. There were no significant differences between the RLA group and the TLA group in terms of age at first diagnosis[44 (35, 54) vs. 46(41, 55) years, P= 0.793], sex (male/female: 3/8 vs. 4/7, P = 1.00), secreting tumor ratio (3/11 vs. 4/11, P = 1.00), tumor location (left/right: 6/6 vs. 7/4, P = 1.00), with hypertension or diabetes mellitus (4/11 vs. 3/11, P = 1.00). However, RLA has significantly smaller tumor size [3.0(2.5, 8.4) cm vs. 7.7(5.2, 8.4)cm, P= 0.001], and more stage Ⅰ patients [90.9%(10/11) vs. 18.2%(2/11), P=0.002], compared with those in TLA group. The perioperative indicators and oncology prognosis outcomes were collected and compared between the two groups. The Kaplan-Meier method was performed to calculate the overall survival (OS) and disease-free survival (DFS). Results:Compared with TLA, RLA had shorter operation time[90(70, 100) vs. 110 (90, 120) min, P = 0.005] and postoperative drainage tube removal time [2 (2, 3) vs. 3 (2, 6) day, P = 0.002), and the difference was statistically significant. In the TLA group, one patient was converted to open operation due to intraoperative tumor capsule rupture. For postoperative complications, one patient in the TLA group suffered with wound infection. There were no perioperative deaths in either group. All postoperative pathological examinations confirmed ACC, and there was no significant difference in Ki-67 index between the two groups [10%(3%, 35%) vs. 10%(9%, 25%), P = 0.484]. The median follow-up was similar in the two groups [48(26, 98) vs. 31(18, 49) months, P=0.237]. The local recurrence and metastasis rates were 36.4% for RLA group and 63.6% for TLA group ( P = 0.395). Survival analysis showed no statistically significant difference in DFS [5-year DFS rate: 33.6% vs. 73.2%, P = 0.118] between the two groups. The 5-year OS rates for RLA group versus TLA group were 58.3% vs. 45.5% ( P=0.485). Conclusions:For localized (stage Ⅰ/Ⅱ) ACC, both RLA and TLA seem safe and feasible, based on the similar long-term oncological prognosis. However, compared with TLA, RLA has the advantage of shorter operation time and postoperative drainage tube removal time. Due to the small number of cases included in this study, further multi-center, large-sample studies are required to demonstrate clear benefit of one surgical approach in the future.

Objective To investigate the protective effect of ADP-ribosylation factor 6 inhibitor on acute kidney injury induced by sepsis in mice.Methods In February 2018,thirty male BALB/c mice were divided into uninfected group (5 mice),fluconazole group (5 mice),ADP-ribosylation factor 6 inhibitor group (10 mice)(inhibitor group) and saline control group (10 mice)(control group) by random number table method.In fluconazole group,inhibitor group and control group,1 × 105 CFU of Candida albicans was injected via tail vein for modeling.The uninfected group was injected with equal volume of saline.After 3 hours,inhibitor group was injected with 1.032 mg ADP-ribosylation factor 6 inhibitor,and fluconazole group was injected with 51 μg fluconazole.The control group were injected with equal volume of saline as inhibitor group.After 24hours,serum creatinine,urea nitrogen were measured by kit method.The mice were clinically scored for sepsis severity according to signs and symptoms after treatment and histopathological changing of kidney tissue were observed and scored according to the damage area of renal cortical with hematoxylin-eosin staining.Results The clinical scores,serum creatinine,urea nitrogen and pathological scores of uninfected group were 0,(0.98 ± 0.38) μmol/L,(9.77 ± 0.36) mmol/L,(0.88 ± 0.30),respectively.The fluconazole group were (0.80 ± 0.84),(1.09 ± 0.51) μmol/L,(9.64 ± 0.17) mmol/L,(1.22 ± 0.270),respectively.The inhibitor group were (2.80 ± 1.32),(1.43 ± 0.50) μmol/L,(12.05 ± 1.20) mmol/L,(2.04 ± 0.55),respectively).The control group were (5.20 ± 1.87),(2.96 ± 1.55) μmol/L,(13.94 ± 1.94) mmoL/L,(2.67±0.55).The difference was statistically significant between inhibitor group and the control group both (P ＜ 0.05).Conclusions ADP-ribosylation factor 6 inhibitor reduce acute kidney injury induced by sepsis in mice.