Recurrence of Hepatocellular Carcinoma in Patients with Low Albumin-Bilirubin Grade in TACE Combined with Ablation: A Random Forest Cox Predictive Model.

Purpose: The aim of our study was to investigate the relationship between albumin-bilirubin (ALBI) grade and recurrence in patients who underwent TACE sequential ablation. We developed and validated a nomogram to predict low levels of ALBI patients' recurrence. Patients and Methods: A total of 880 patients undergoing TACE combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were retrospectively enrolled, including 415 patients with L-ALBI (≤-2.6) and 465 patients with high levels (>-2.6) of ALBI (H-ALBI). L-ALBI patients were randomized in a 7:3 ratio into the training cohort (N=289) and validation cohort (N=126). Multivariate Cox regression followed by random survival forest was carried out to identify independent risk factors for prediction nomogram construction. An examination of nomogram accuracy was performed using the C-index, receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA) curves. According to the nomogram, the patients were divided into low-risk, intermediate-risk, and high-risk groups. Kaplan-Meier (KM) curves were applied to compare the difference in recurrence-free survival (RFS) among the three groups. Results: The median RFS in L-ALBI patients was significantly longer than the H-ALBI patients (40.8m vs 20.1m, HR:1.71, 95% CI:1.44-2.04, P<0.0001). The nomogram was composed of five variables, such as age, Barcelona Clinic Liver Cancer (BCLC) stage, globulin, gamma-glutamyl transferase to lymphocyte ratio (GLR), and international normalized ratio (INR). The C-index (0.722 and 0.731) and 1-, 3-, and 5-year AUCs (0.725, 0.803, 0.870, and 0.764, 0.816, 0.798) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves demonstrated good consistency and good clinical utility. There were significant differences in RFS between the low-risk, intermediate-risk, and high-risk groups (P<0.0001). Conclusion: L-ALBI Patients who underwent TACE combined ablation had better recurrence-free survival than patients with H-ALBI. The nomogram developed and validated in our study had good predictive ability in recurrence for L-ALBI patients.

Prognostic nomogram based on the gamma-glutamyl transpeptidase-to-platelet ratio for patients with compensated cirrhotic hepatocellular carcinoma after local ablation.

Background: Hepatocellular carcinoma (HCC) patients with compensated cirrhosis typically face a high prevalence and unfavorable prognosis. However, there is currently a deficiency in prediction models to anticipate the prognosis of these patients. Therefore, our study included the Gamma-glutamyl transpeptidase-to-platelet ratio (GPR) in analysis and aimed to develop a nomogram for HCC patients with compensated cirrhosis after local ablation. Methods: Enrolling 669 patients who underwent local ablation at Beijing You'an Hospital during the period from January 1, 2014, to December 31, 2022, this study focused on individuals with compensated cirrhotic HCC. In a ratio of 7:3, patients were allocated to the training cohort (n=468) and the validation cohort (n=201). Lasso-Cox regression was employed to identify independent prognostic factors for overall survival (OS). Subsequently, a nomogram was constructed using these factors and was validated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: GPR, age, and hemoglobin were identified by Lasso-Cox regression as independent prognostic factors of the nomogram. The area under the ROC curves (AUCs) for 3-, 5-, and 8-year OS (0.701, 0.755, and 0.768 for the training cohort; 0.684, 0.707, and 0.778 for the validation cohort), and C-indices (0.695 for training cohort; 0.679 for validation cohort) exhibited the excellent predictive ability of the nomogram. Calibration curves and DCA curves indicated favorable calibration performance and clinical utility. Patients were further stratified into two risk groups according to the median nomogram score. There existed an obvious distinction between the two groups both in the training cohort and validation cohort. Conclusion: In summary, this research established and validated a novel nomogram to predict OS, which had good predictive power for HCC patients with compensated cirrhosis after local ablation.

Incorporating Inflammatory Markers and Clinical Indicators into a Predictive Model of Single Small Hepatocellular Carcinoma Recurrence After Primary Locoregional Treatments.

Purpose: We explored the role of tumor size and number in the prognosis of HCC patients who underwent ablation and created a nomogram based on machine learning to predict the recurrence. Patients and Methods: A total of 990 HCC patients who underwent transcatheter arterial chemoembolization (TACE) combined ablation at Beijing Youan Hospital from January 2014 to December 2021 were prospectively enrolled, including 478 patients with single small HCC (S-S), 209 patients with single large (≥30mm) HCC (S-L), 182 patients with multiple small HCC (M-S), and 121 patients with multiple large HCC (M-L). S-S patients were randomized in a 7:3 ratio into the training cohort (N=334) and the validation cohort (N=144). Lasso-Cox regression analysis was carried out to identify independent risk factors, which were used to construct a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Patients in the training and validation cohorts were divided into low-risk, intermediate-risk, and high-risk groups based on the risk scores of the nomogram. Results: The median recurrence-free survival (mRFS) in S-S patients was significantly longer than the S-L, M-S, and S-L patients (P<0.0001). The content of the nomogram includes age, monocyte-to-lymphocyte (MLR), gamma-glutamyl transferase-to-lymphocyte (GLR), International normalized ratio (INR), and Erythrocyte (RBC). The C-index (0.704 and 0.71) and 1-, 3-, and 5-year AUCs (0.726, 0.800, 0.780, and 0.752, 0.761, 0.760) of the training and validation cohorts proved the excellent predictive performance of the nomogram. Calibration curves the DCA curves showed that the nomogram had good consistency and clinical utility. There were apparent variances in RFS between the low-risk, intermediate-risk, and high-risk groups (P<0.0001). Conclusion: S-S patients who underwent ablation had the best prognosis. The nomogram developed and validated in the study had good predictive ability for S-S patients.

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

Introduction: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment. Methods: We gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You'an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores: low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts: training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance. Results: The study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram's ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Conclusion: Our study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy.

A Novel Nomogram to Predict Prognosis in Elderly Early-Stage Hepatocellular Carcinoma Patients After Ablation Therapy.

Purpose: Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Early diagnosis is crucial for improving prognosis. Elderly HCC patients often have underlying liver diseases such as chronic hepatitis and cirrhosis, leading to impaired liver function and suboptimal liver reserve. Radiofrequency ablation (RFA) has rapidly become one of the most important methods for treating early-stage hepatocellular carcinoma (EHCC) due to its advantages, including minimal trauma, short operation time, less intraoperative bleeding, quick postoperative recovery, cost-effectiveness, and few postoperative-complications. However, the prognostic model for early recurrence after local ablation in elderly EHCC patients has not been widely evaluated. We have developed a prognostic model for the recurrence of local RFA in elderly EHCC patients. This is expected to provide a new early warning system for preventing early recurrence in elderly EHCC patients, prolonging patient's life, and improving overall quality of life. Methods: In this study, we included 661 EHCC patients who underwent local ablation, dividing them into a Primary cohort and a Validation cohort in a 7:3 ratio. We characterized the cohorts and utilized the primary cohort to develop a prognostic nomogram model for recurrence after local ablation in elderly EHCC patients. Additionally, the validation cohort was used to assess the potential of the nomogram as a non-invasive biomarker for post-ablation recurrence in EHCC. Results: The user-friendly nomogram incorporates common clinical variables including gender, BCLC stage, tumor number, tumor size, red blood cell (RBC), gamma-glutamyl transferase (GGT), and prothrombin time activity (PTA). The nomogram constructed using the identified seven variables exhibits robust discriminatory capabilities, favorable predictive performance, and noteworthy clinical utility. Conclusion: We developed a user-friendly nomogram based on the BCLC stage classification, which may provide prognostic assessments for elderly EHCC patients at 1, 3, and 5 years post-RFA.

GALAD score as a prognostic model for recurrence of hepatocellular carcinoma after local ablation.

BACKGROUND: Currently, the high recurrence rate still forms severe challenges in hepatocellular carcinoma (HCC) treatment. The GALAD score, including age, gender, alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), and des-gamma-carboxyprothrombin (DCP) was developed as a diagnostic model. However, evidence is still lacking to confirm the capability of the GALAD score to predict the recurrence of HCC. METHODS: This study included 390 HCC patients after local ablation at Beijing You'an Hospital from January 1, 2018, to December 31, 2022. Firstly, the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the predictive capability of the GALAD score. Then, the Kaplan-Meier (KM) curve and log-rank test were used to compare the prognosis between two groups classified by GALAD score. Finally, a nomogram for high-risk patients was established by Lasso-Cox regression. It was assessed by ROC curves, calibration curves, and decision curve analysis (DCA). RESULTS: The ROC curve (AUC: 0.749) and KM curve showed the GALAD score had good predictive ability and could clearly stratify patients into two groups through the risk of recurrence. Prognostic factors selected by Lasso-Cox regression contained tumor number, tumor size, and globulin. The nomogram for high-risk patients showed reliable discrimination, calibration, and clinical utility. CONCLUSION: This research displayed that the GALAD score is an effective model for predicting the recurrence of HCC. Meanwhile, we found the poor prognosis of the high-risk group and created a nomogram for these patients.

Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis.

Introduction: This study conducts a retrospective analysis on patients with BCLC stage A/B hepatocellular carcinoma (HCC) accompanied by Child-Pugh B cirrhosis, who underwent transarterial chemoembolization (TACE) in combination with local ablation therapy. Our goal was to uncover risk factors contributing to post-treatment recurrence and to develop and validate an innovative 1-, 3-, and 5-year recurrence free survival (RFS) nomogram. Methods: Data from 255 BCLC A/B HCC patients with Child-Pugh B cirrhosis treated at Beijing You'an Hospital (January 2014 - January 2020) were analyzed using random survival forest (RSF), LASSO regression, and multivariate Cox regression to identify independent risk factors for RFS. The prognostic nomogram was then constructed and validated, categorizing patients into low, intermediate, and high-risk groups, with RFS assessed using Kaplan-Meier curves. Results: The nomogram, integrating the albumin/globulin ratio, gender, tumor number, and size, showcased robust predictive performance. Harrell's concordance index (C-index) values for the training and validation cohorts were 0.744 (95% CI: 0.703-0.785) and 0.724 (95% CI: 0.644-0.804), respectively. The area under the curve (AUC) values for 1-, 3-, and 5-year RFS in the two cohorts were also promising. Calibration curves highlighted the nomogram's reliability and decision curve analysis (DCA) confirmed its practical clinical benefits. Through meticulous patient stratification, we also revealed the nomogram's efficacy in distinguishing varying recurrence risks. Conclusion: This study advances recurrence prediction in BCLC A/B HCC patients with Child-Pugh B cirrhosis following TACE combined with ablation. The established nomogram accurately predicts 1-, 3-, and 5-year RFS, facilitating timely identification of high-risk populations.

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation.

Introduction: To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients' recurrence risk. Methods: Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You'an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors. Results: Age, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves. Conclusion: This study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study.

Background: Hepatitis B surface antigen (HBsAg) clearance is associated with improved long-term outcomes and reduced risk of complications. The aim of our study was to identify the effects of levels of HBsAg in HCC patients undergoing TACE and sequential ablation. In addition, we created a nomogram to predict the prognosis of HCC patients with high levels of HBsAg (≥1000U/L) after local treatment. Method: This study retrospectively evaluated 1008 HBV-HCC patients who underwent TACE combined with ablation at Beijing Youan Hospital and Beijing Ditan Hospital from January 2014 to December 2021, including 334 patients with low HBsAg levels and 674 patients with high HBsAg levels. The high HBsAg group was divided into the training cohort (N=385), internal validation cohort (N=168), and external validation cohort (N=121). The clinical and pathological features of patients were collected, and independent risk factors were identified using Lasso-Cox regression analysis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Patients were classified into high-risk and low-risk groups based on the risk scores of the nomogram. Result: After PSM, mRFS was 28.4 months (22.1-34.7 months) and 21.9 months (18.5-25.4 months) in the low HBsAg level and high HBsAg level groups (P<0.001). The content of the nomogram includes age, BCLC stage, tumor size, globulin, GGT, and bile acids. The C-index (0.682, 0.666, and 0.740) and 1-, 3-, and 5-year AUCs of the training, internal validation, and external validation cohorts proved good discrimination of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classification of patients with high HBsAg levels into low-risk and high-risk groups according to the risk of recurrence. There was a statistically significant difference in RFS between the two groups in the training, internal validation, and external validation cohorts (P<0.001). Conclusion: High levels of HBsAg were associated with tumor progression. The nomogram developed and validated in the study had good predictive ability for patients with high HBsAg levels.

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy.

Purpose: The aim of the study is to identify and evaluate multifaceted factors impacting the survival of elderly cirrhotic HCC patients following ablation therapy, with the goal of constructing a nomogram to predict their 3-, 5-, and 8-year overall survival (OS). Patients and Methods: A retrospective analysis was conducted on 736 elderly cirrhotic HCC patients who underwent ablation therapy between 2014 and 2022. LASSO regression, random survival forest (RSF), and multivariate Cox analyses were employed to identify independent prognostic factors for OS, followed by the development and validation of a predictive nomogram. Harrell's concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to assess the performance of the nomogram. The nomogram was finally utilized to stratify patients into low-, intermediate-, and high-risk groups, aiming to assess its efficacy in precisely discerning individuals with diverse overall survival outcomes. Results: Alcohol drinking, tumor number, globulin (Glob) and prealbumin (Palb) were identified and integrated to establish a novel prognostic nomogram. The nomogram exhibited strong discriminative ability with C-indices of 0.723 (training cohort) and 0.693 (validation cohort), along with significant Area Under the Curve (AUC) values for 3-year, 5-year, and 8-year OS in both cohorts (0.758, 0.770, and 0.811 for training cohort; 0.744, 0.699 and 0.737 for validation cohort). Calibration plots substantiated its consistency, while DCA curves corroborated its clinical utility. The nomogram further demonstrated exceptional effectiveness in discerning distinct risk populations, highlighting its robust applicability for prognostic stratification. Conclusion: Our study successfully developed and validated a robust nomogram model based on four key clinical parameters for predicting 3-, 5- and 8-year OS among elderly cirrhotic HCC patients following ablation therapy. The nomogram exhibited a remarkable capability in identifying high-risk patients, furnishing clinicians with invaluable insights for postoperative surveillance and tailored therapeutic interventions.

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics.

Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals. Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You'an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method. Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064). Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.

Introduction: This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS). Methods: The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results: The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients. Conclusions: The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.

Development and Validation of a Prognostic Nomogram for Patients with AFP and DCP Double-Negative Hepatocellular Carcinoma After Local Ablation.

Purpose: Although alpha-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP) have a certain predictive ability for the prognosis of hepatocellular carcinoma (HCC), there are still some cases of aggressive recurrence among patients with AFP and DCP double-negative HCC (DNHC) after local ablation. However, prediction models to forecast the prognosis of DNHC patients are still lacking. Thus, this retrospective study aims to explore the prognostic factors in DNHC patients and develop a nomogram to predict recurrence. Patients and methods: 493 DNHC patients who underwent the local ablation at Beijing You'an Hospital between January 1, 2014, and December 31, 2022, were enrolled. A part that was admitted from January 1, 2014, to December 31, 2018, was designated to the training cohort (n = 307); others from January 1, 2019, to December 31, 2022, were allocated to the validation cohort (n = 186). Lasso regression and Cox regression were employed with the aim of screening risk factors and developing the nomogram. The nomogram outcome was assessed by discrimination, calibration, and decision curve analysis (DCA). Results: Independent prognostic factors selected by Lasso-Cox analysis included age, tumor size, tumor number, and gamma-glutamyl transferase. The area under the receiver operating characteristic (ROC) curves (AUCs) of the training and validation groups (0.738, 0.742, 0.836, and 0.758, 0.821) exhibited the excellent predicted outcome of the nomogram. Calibration plots and DCA plots suggest desirable calibration performance and clinical utility. Patients were stratified into three risk groups by means of the nomogram: low-risk, intermediate-risk, and high-risk, respectively. There exists an obvious distinction in recurrence-free survival (RFS) among three groups (p<0.0001). Conclusion: In conclusion, we established and validated a nomogram for DNHC patients who received local ablation. The nomogram showed excellent predictive power for the recurrence of HCC and could contribute to guiding clinical decisions.

Development and Validation of a Nomogram to Predict the Recurrence of HCC Patients Undergoing CECT After Ablation.

Purpose: We first aimed to compare the prognostic difference between the application of Contrast-enhanced computed tomography (CECT) and Non-enhanced computed tomography (NECT) in hepatocellular carcinoma(HCC) patients with early-stage immediately after ablation. We secondly propose to explore the risk factors for recurrence in patients undergoing CECT, and then develop a nomogram. Patients and Methods: Clinical data were collected from 711 patients who received TACE combined with ablation from January 1, 2015, to December 31, 2022, at Beijing Youan Hospital. According to the imaging methods applied after ablation, patients were categorized into the CECT group and the NECT group and then were compared by Kaplan-Meier (KM) curves. Lasso regression is used to screen risk factors for recurrence and the nomogram was plotted. Finally, discrimination, calibration plot, and decision curve analysis (DCA) were used to measure the performance of the nomogram. Results: The KM curve indicates that recurrence-free survival (RFS) was longer in the CECT group than in the NECT group (HR =0.759, 95% CI 0.606-0.951, P=0.016). Six variables were selected to construct the nomogram. 1-, 3-, and 5-year area under the curves (AUCs) (0.867, 0.731, 0.773 and 0.896, 0.784, 0.773) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classify of patients into three groups according to the risk of recurrence: low risk, intermediate risk, and high risk. There was a statistically significant difference in RFS between the two groups in the training and validation cohorts (P<0.001). Conclusion: We demonstrated that HCC patients who underwent CECT evaluation after ablation had a better prognosis, making this evaluation method highly recommended for guiding clinical management.

Total flavonoids of Litchi chinensis Sonn. seed inhibit prostate cancer growth in bone by regulating the bone microenvironment via inactivation of the HGFR/NF-κB signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Litchi chinensis Sonn. (Litchi) seed, a traditional Chinese medicine, is habitually used in the clinical treatment of prostate cancer (PCa)-induced bone pain. In our previous study, flavonoids have been identified as the active ingredient of litchi seed against PCa. However, its anti-tumor activities in bone and associated molecular mechanisms are still unclear. AIM OF THE STUDY: To investigate the effects and underlying mechanisms of total flavonoids of litchi seed (TFLS) on the growth of PCa in bone. MATERIALS AND METHODS: The effect of TFLS on the growth of PCa in bone was observed using a mouse model constructed with tibial injection of luciferase-expressing RM1-luc cells. Conditioned medium (CM) from bone marrow stromal cells OP9 and CM treated with TFLS (T-CM) was used to investigate the effect on the proliferation, colony formation, and apoptosis of PCa cells (LNCaP, PC3, RM1). An antibody microarray was performed to detect cytokine expression in the supernatant fraction of OP9 cell cultures treated with TFLS or left untreated. Western blot assay was employed to determine the expression and activity of HGFR and its key downstream proteins, Akt, mTOR, NF-κB, and Erk, in PCa cells. The potential target was further verified using immunofluorescence and immunohistochemistry assays. RESULTS: Treatment with TFLS (80 mg/kg, 24 days) significantly suppressed the growth of RM1 cells in bone. CM from bone marrow stromal cells OP9 stimulated the proliferation and colony formation of the PCa cells as well as inhibited the apoptosis of PC3 cells, while T-CM reversed the effects mediated by OP9 cells in vitro. In an antibody array assay, TFLS regulated the majority of cytokines in OP9 cell culture supernatant, among which HGF, HGFR, IGF-1R, and PDGF-AA showed the greatest fold changes. Mechanistically, CM upregulated HGFR and promoted phosphorylation of NF-κB while T-CM induced reduction of HGFR and dephosphorylation of NF-κB in PC3 cells. Moreover, T-CM inhibited NF-κB entry into PC3 cell nuclei. Data from in vivo experiments further confirmed the inhibitory effects of TFLS on NF-κB. CONCLUSION: TFLS suppresses the growth of PCa in bone through regulating bone microenvironment and the underlying mechanism potentially involves attenuation of the HGFR/NF-κB signaling axis.

Dual gene-activated dermal scaffolds regulate angiogenesis and wound healing by mediating the coexpression of VEGF and angiopoietin-1.

The vascularization of dermal substitutes is a key challenge in efforts to heal deep skin defects. In this study, dual gene-activated dermal scaffolds (DGADSs-1) were fabricated by loading nanocomposite particles of polyethylenimine (PEI)/multiple plasmid DNAs (pDNAs) encoding vascular endothelial growth factor and angiopoietin-1 at a ratio of 1:1. In a similar manner, DGADSs-2 were loaded with a chimeric plasmid encoding both VEGF and Ang-1. In vitro studies showed that both types of DGADSs released PEI/pDNA nanoparticles in a sustained manner; they demonstrated effective transfection ability, leading to upregulated expression of VEGF and Ang-1. Furthermore, both types of DGADSs promoted fibroblast proliferation and blood vessel formation, although DGADSs-1 showed a more obvious promotion effect. A rat full-thickness skin defect model showed that split-thickness skin transplanted using a one-step method could achieve full survival at the 12th day after surgery in both DGADSs-1 and DGADSs-2 groups, and the vascularization time of dermal substitutes was significantly shortened. Compared with the other three groups of scaffolds, the DGADSs-1 group had significantly greater cell infiltration, collagen deposition, neovascularization, and vascular maturation, all of which promoted wound healing. Thus, compared with single-gene-activated dermal scaffolds, DGADSs show greater potential for enhancing angiogenesis. DGADSs with different loading modes also exhibited differences in terms of angiogenesis; the effect of loading two genes (DGADSs-1) was better than the effect of loading a chimeric gene (DGADSs-2). In summary, DGADSs, which continuously upregulate VEGF and Ang-1 expression, offer a new functional tissue-engineered dermal substitute with the ability to activate vascularization.

Triage and Evaluation of Blast-Injured Patients in Wenling Liquefied Petroleum Gas Tanker Explosion.

On June 13, 2020, a liquefied petroleum gas tanker exploded in Wenling. Here, the authors describe the mass casualty emergency response to the explosion. The authors collected the medical records of 176 inpatients at 8 hospitals in Taizhou and Hangzhou. The 176 inpatients with blast injuries comprised 70 females and 106 males, with an average age of 45.48 ± 19.96 years, and more than half of the patients were farmers. They were transported to six hospitals distributed around the explosion site in Taizhou in the initial rescue period and were grouped according to their new injury severity score as having mild, moderate, severe, or extremely severe injuries. Most patients with severe and extremely severe injuries were admitted to a superior hospital for postsecondary triage. Forty-four patients experienced primary blast injuries, 137 experienced secondary blast injuries, 37 experienced tertiary blast injuries, and 40 patients experienced quaternary blast injuries. Multiple blast injuries were suffered by 62 patients. Most patients (95.45%) suffered external injuries, with the chest, extremities, and face as the main affected areas. Burns were diagnosed in 26 adults, of whom 15.38%, 19.23%, 7.70%, and 57.69% suffered mild, moderate, severe, and extremely severe cases. Sixteen burn patients suffered from burn-blast injuries. Upper limbs and the head/face/neck area, as exposed areas, were more likely to experience a burn injury. Inhalation was the main accompanying injury. Of the eight patients who died in the prehospital session, seven had burn injuries. This report on the accident and injury characteristics of an open-air LPG-related explosion will facilitate responses to subsequent catastrophes.

Chlorogenic Acid Inhibits Epithelial-Mesenchymal Transition and Invasion of Breast Cancer by Down-Regulating LRP6.

Epithelial-mesenchymal transition (EMT) is a crucial biologic process for breast cancer metastasis, and inhibition of EMT could be an effective approach to suppress metastatic potential of mammary cancer. High expression of low-density lipoprotein receptor-related protein 6 (LRP6) is usually observed in breast carcinoma and predicts poor prognosis. In the present study, we investigated whether chlorogenic acid (CA) can inhibit the EMT of breast cancer cells and underlying molecular mechanism. We found that CA treatment transformed MCF-7 cell morphology from spindle shape (mesenchymal phenotype) to spherical shape (epithelial phenotype). CA clearly increased epithelial biomarkers' expression (E-cadherin and ZO-1) but decreased mesenchymal proteins' expression (ZEB1, N-cadherin, vimentin, snail, and slug). In addition, CA attenuated MMP-2 and MMP-9 activities and inhibited cell migration and invasion. CA downregulated the expression of LRP6 in MCF-7 cells. Knockdown LRP6 with siRNA repressed cell mobility and invasion, wheras overexpression of LRP6 promoted EMT and antagonized the EMT inhibitory effect of CA on MCF-7 cells. Furthermore, CA directly interacted with Wnt/ß-catenin signaling coreceptor LRP6 and reduced LRP6, p-LRP6, and ß-catenin expression levels in MCF-7 cells. In vivo study revealed that CA notably reduced tumor volume and tumor weight. CA decreased the expression of LRP6, N-cadherin, ZEB1, vimentin, MMP2, MMP9, and increased the expression of E-cadherin and ZO-1. In conclusion, CA inhibited EMT and invasion of breast cancer by targeting LRP6. SIGNIFICANCE STATEMENT: CA, the familiar polyphenol compound in traditional Chinese medicine, repressed EMT and weakened cellular mobility and invasion in MCF-7 cells. The mechanism studies demonstrated that CA could inhibit EMT and invasion of MCF-7 cells via targeting LRP6. Additionally, CA restrained tumor growth and xenograft tumor EMT in vivo. The EMT inhibitory property of CA warrants further studies of CA as a drug candidate for the therapy of metastatic breast carcinoma.

Respiratory immune status and microbiome in recovered COVID-19 patients revealed by metatranscriptomic analyses.

Coronavirus disease 2019 (COVID-19) is currently a severe threat to global public health, and the immune response to COVID-19 infection has been widely investigated. However, the immune status and microecological changes in the respiratory systems of patients with COVID-19 after recovery have rarely been considered. We selected 72 patients with severe COVID-19 infection, 57 recovered from COVID-19 infection, and 65 with non-COVID-19 pneumonia, for metatranscriptomic sequencing and bioinformatics analysis. Accordingly, the differentially expressed genes between the infected and other groups were enriched in the chemokine signaling pathway, NOD-like receptor signaling pathway, phagosome, TNF signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and C-type lectin receptor signaling pathway. We speculate that IL17RD, CD74, and TNFSF15 may serve as disease biomarkers in COVID-19. Additionally, principal coordinate analysis revealed significant differences between groups. In particular, frequent co-infections with the genera Streptococcus, Veillonella, Gemella, and Neisseria, among others, were found in COVID-19 patients. Moreover, the random forest prediction model with differential genes showed a mean area under the curve (AUC) of 0.77, and KCNK12, IL17RD, LOC100507412, PTPRT, MYO15A, MPDZ, FLRT2, SPEG, SERPINB3, and KNDC1 were identified as the most important genes distinguishing the infected group from the recovered group. Agrobacterium tumefaciens, Klebsiella michiganensis, Acinetobacter pittii, Bacillus sp. FJAT.14266, Brevundimonas naejangsanensis, Pseudopropionibacterium propionicum, Priestia megaterium, Dialister pneumosintes, Veillonella rodentium, and Pseudomonas protegens were selected as candidate microbial markers for monitoring the recovery of COVID patients. These results will facilitate the diagnosis, treatment, and prognosis of COVID patients recovering from severe illness.