Prognostic factors in patients with thymoma who underwent surgery.

PURPOSE: Thymoma is the most common primary tumor in the anterior mediastinum. The prognostic factors of patients with thymoma still need to be clarified. In this study, we aimed to investigate the prognostic factors of patients with thymoma who received radical resection and establish the nomogram to predict the prognosis of these patients. MATERIALS AND METHODS: Patients who underwent radical resection for thymoma with complete follow-up data between 2005 and 2021 were enrolled. Their clinicopathological characteristics and treatment methods were retrospectively analyzed. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared by the log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify the independent prognostic factors. According to the results of the univariate analysis in the Cox regression model, the predictive nomograms were created. RESULTS: A total of 137 patients with thymoma were enrolled. With a median follow-up of 52 months, the 5-year and 10-year PFS rates were 79.5% and 68.1%, respectively. The 5-year and 10-year OS rates were 88.4% and 73.1%, respectively. Smoking status (P = 0.022) and tumor size (P = 0.039) were identified as independent prognostic factors for PFS. Multivariate analysis showed that a high level of neutrophils (P = 0.040) was independently associated with OS. The nomogram showed that the World Health Organization (WHO) histological classification contributed more to the risk of recurrence than other factors. Neutrophil count was the most important predictor of OS in patients with thymoma. CONCLUSION: Smoking status and tumor size are risk factors for PFS in patients with thymoma. A high level of neutrophils is an independent prognostic factor for OS. The nomograms developed in this study accurately predict PFS and OS rates at 5 and 10 years in patients with thymoma based on individual characteristics.

Silencing UBQLN2 Enhances the Radiosensitivity of Esophageal Squamous Cell Carcinoma (ESCC) via Activating p38 MAPK.

Background: Ubiquilin 2 (UBQLN2) is an adaptor of ubiquitinated proteins and the proteasome. The potential role of UBQLN2 in carcinogenesis has been demonstrated. However, its role in modulating the radiosensitivity of cancer is not clear. Here, we explored the radiosensitizing effect of silencing UBQLN2 on esophageal squamous cell carcinoma (ESCC) and its mechanisms. Methods: We analyzed the prognostic role of UBQLN2 in the ESCC patient cohort from the Cancer Genomic Atlas (TCGA) database and our hospital. We also conducted a series of experiments in vivo and in vitro to investigate the effect of silencing UBQLN2 on ESCC radiosensitivity and its mechanisms. Results: UBQLN2 is highly expressed in ESCC tissues and positively correlated with poor overall survival (OS). The knockdown of UBQLN2 dramatically increased the radiosensitivity of ESCC cells. Mechanically, UBQLN2 suppression substantially upregulated p38 mitogen-activated protein kinases (MAPK). The p38 MAPK inhibitor SB203580 could reverse the radiation-enhancing effect induced by UBQLN2 knockdown. The direct interaction between UBQLN2 and p38 MAPK was confirmed by co-immunoprecipitation (CO-IP) assay. Furthermore, silencing UBQLN2 also inhibited the expression of phosphorylated DNA-dependent protein kinase catalytic subunit (p-DNA-PKcs) after irradiation. Finally, the xenografted tumor experiment confirmed the radiosensitizing effect of silencing UBQLN2 on ESCC in vivo. Conclusion: Our results suggest that silencing UBQLN2 enhances the radiosensitivity of ESCC by activating p38 MAPK, and UBQLN2 may be a potential target to enhance the radiosensitivity of ESCC.

Clinical characteristics and patient outcomes of molecular subtypes of small cell lung cancer (SCLC).

BACKGROUND: Recent studies have shown that according to the expression levels of achaete-scute homolog 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), and POU class 2 homeobox 3 (POU2F3), small cell lung cancer (SCLC) can be divided into four subtypes: SCLC-A (ASCL1-dominant), SCLC-N (NEUROD1-dominant), SCLC-P (POU2F3-dominant), and SCLC-I (triple negative or SCLC-inflamed). However, there are limited data on the clinical characteristics and prognosis of molecular subtypes of SCLC. METHODS: Immunohistochemistry (IHC) was used to detect the expression levels of ASCL1, NEUROD1, and POU2F3 in 53 patient samples of resectable SCLC. The subtype was defined by the differential expression of the transcription factors for ASCL1, NEUROD1, and POU2F3 or the low expression of all three factors with an inflamed gene signature (SCLC-A, SCLC-N, SCLC-P, and SCLC-I, respectively). The clinicopathological characteristics, immunological features (programmed death ligand 1 [PD-L1] expression and CD8+ tumor infiltrating lymphocyte [TIL] density), and patient outcomes of the four subtypes of SCLC were analyzed. RESULTS: Positive ASCL1, NEUROD1, and POU2F3 staining was detected in 43 (79.2%), 27 (51.0%), and 17 (32.1%) SCLC specimens by IHC. According to the results of IHC analysis, SCLC was divided into four subtypes: SCLC-A (39.6%), SCLC-N (28.3%), SCLC-P (17.0%), and SCLC-I (15.1%). The 5-year overall survival (OS) rates of these four subtypes were 61.9%, 69.3%, 41.7%, and 85.7%, respectively (P=0.251). There were significant differences in smoking status among different subtypes of SCLC (P= 0.031). However, we did not confirm the correlation between subtypes of SCLC and other clinicopathological factors or immune profiles. Cox multivariate analysis showed that N stage (P=0.025), CD8+ TILs (P=0.024), Ki-67 level (P=0.040), and SCLC-P (P=0.023) were independent prognostic factors for resectable SCLC. CONCLUSIONS: Our IHC-based study validated the proposed classification of SCLC using the expression patterns of key transcriptional regulatory factors. We found that SCLC-P was associated with smokers and was one of the poor prognostic factors of limited-stage SCLC. In addition, no correlation was found between PD-L1 expression or CD8+ TIL density and SCLC subtypes.

A Simple and Quick Screening Method for Intrapulmonary Vascular Dilation in Cirrhotic Patients Based on Machine Learning.

BACKGROUND AND AIMS: Screening for hepatopulmonary syndrome in cirrhotic patients is limited due to the need to perform contrast enhanced echocardiography (CEE) and arterial blood gas (ABG) analysis. We aimed to develop a simple and quick method to screen for the presence of intrapulmonary vascular dilation (IPVD) using noninvasive and easily available variables with machine learning (ML) algorithms. METHODS: Cirrhotic patients were enrolled from our hospital. All eligible patients underwent CEE, ABG analysis and physical examination. We developed a two-step model based on three ML algorithms, namely, adaptive boosting (termed AdaBoost), gradient boosting decision tree (termed GBDT) and eXtreme gradient boosting (termed Xgboost). Noninvasive variables were input in the first step (the NI model), and for the second step (the NIBG model), a combination of noninvasive variables and ABG results were used. Model performance was determined by the area under the curve of receiver operating characteristics (AUCROCs), precision, recall, F1-score and accuracy. RESULTS: A total of 193 cirrhotic patients were ultimately analyzed. The AUCROCs of the NI and NIBG models were 0.850 (0.738-0.962) and 0.867 (0.760-0.973), respectively, and both had an accuracy of 87.2%. For both negative and positive cases, the recall values of the NI and NIBG models were both 0.867 (0.760-0.973) and 0.875 (0.771-0.979), respectively, and the precisions were 0.813 (0.690-0.935) and 0.913 (0.825-1.000), respectively. CONCLUSIONS: We developed a two-step model based on ML using noninvasive variables and ABG results to screen for the presence of IPVD in cirrhotic patients. This model may partly solve the problem of limited access to CEE and ABG by a large numbers of cirrhotic patients.

Hepatopulmonary syndrome delays postoperative recovery and increases pulmonary complications after hepatectomy.

BACKGROUND: This study attempted to investigate the impact of hepatopulmonary syndrome (HPS) on postoperative outcomes in hepatitis B virus-induced hepatocellular carcinoma (HBV-HCC) patients. METHODS: HBV-HCC patients undergoing primary curative hepatectomy for HCC in our hospital were diagnosed with HPS by contrast-enhanced echocardiography (CEE) and arterial blood gas analysis. Patients were divided into HPS, intrapulmonary vascular dilation (IPVD) (patients with positive CEE results and normal oxygenation) and control (patients with negative CEE results) groups. Baseline information, perioperative clinical data and postoperative pulmonary complications (PPCs) were compared among all groups. Cytokines in patient serums from each group (n = 8) were also assessed. RESULTS: Eighty-seven patients undergoing hepatectomy from October 2019 to January 2020 were analyzed. The average time in the postanaesthesia care unit (112.10 ± 38.57 min) and oxygen absorption after extubation [34.0 (14.5-54.5) min] in the HPS group was longer than in IPVD [81.81 ± 26.18 min and 16.0 (12.3-24.0) min] and control [93.70 ± 34.06 min and 20.5 (13.8-37.0) min] groups. There were no significant differences in oxygen absorption time after extubation between HPS and control groups. The incidence of PPCs, especially bi-lateral pleural effusions in the HPS group (61.9%), was higher than in IPVD (12.5%) and control (30.0%) groups. Increased serum levels of the growth-regulated oncogene, monocyte chemoattractant protein, soluble CD40 ligand and interleukin 8 might be related to delayed recovery in HPS patients. CONCLUSIONS: HPS patients with HBV-HCC suffer delayed postoperative recovery and are at higher risk for PPCs, especially bi-lateral pleural effusions, which might be associated with changes in certain cytokines.

A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence.

BACKGROUND: Dynamic and precise estimation of blood loss (EBL) is quite important for perioperative management. To date, the Triton System, based on feature extraction technology (FET), has been applied to estimate intra-operative haemoglobin (Hb) loss but is unable to directly assess the amount of blood loss. We aimed to develop a method for the dynamic and precise EBL and estimate Hb loss (EHL) based on artificial intelligence (AI). METHODS: We collected surgical patients' non-recycled blood to generate blood-soaked sponges at a set gradient of volume. After image acquisition and preprocessing, FET and densely connected convolutional networks (DenseNet) were applied for EBL and EHL. The accuracy was evaluated using R2, the mean absolute error (MAE), the mean square error (MSE), and the Bland-Altman analysis. RESULTS: For EBL, the R2, MAE and MSE for the method based on DenseNet were 0.966 (95% CI: 0.962-0.971), 0.186 (95% CI: 0.167-0.207) and 0.096 (95% CI: 0.084-0.109), respectively. For EHL, the R2, MAE and MSE for the method based on DenseNet were 0.941 (95% CI: 0.934-0.948), 0.325 (95% CI: 0.293-0.355) and 0.284 (95% CI: 0.251-0.317), respectively. The accuracies of EBL and EHL based on DenseNet were more satisfactory than that of FET. Bland-Altman analysis revealed a bias of 0.02 ml with narrow limits of agreement (LOA) (-0.47 to 0.52 mL) and of 0.05 g with narrow LOA (-0.87 to 0.97 g) between the methods based on DenseNet and actual blood loss and Hb loss. CONCLUSIONS: We developed a simpler and more accurate AI-based method for EBL and EHL, which may be more fit for surgeries primarily using sponges and with a small to medium amount of blood loss.