F-box proteins and gastric cancer: an update from functional and regulatory mechanism to therapeutic clinical prospects.

Gastric cancer (GC) is a prevalent malignancy characterized by significant morbidity and mortality, yet its underlying pathogenesis remains elusive. The etiology of GC is multifaceted, involving the activation of oncogenes and the inactivation of antioncogenes. The ubiquitin-proteasome system (UPS), responsible for protein degradation and the regulation of physiological and pathological processes, emerges as a pivotal player in GC development. Specifically, the F-box protein (FBP), an integral component of the SKP1-Cullin1-F-box protein (SCF) E3 ligase complex within the UPS, has garnered attention for its prominent role in carcinogenesis, tumor progression, and drug resistance. Dysregulation of several FBPs has recently been observed in GC, underscoring their significance in disease progression. This comprehensive review aims to elucidate the distinctive characteristics of FBPs involved in GC, encompassing their impact on cell proliferation, apoptosis, invasive metastasis, and chemoresistance. Furthermore, we delve into the emerging role of FBPs as downstream target proteins of non-coding RNAs(ncRNAs) in the regulation of gastric carcinogenesis, outlining the potential utility of FBPs as direct therapeutic targets or advanced therapies for GC.

Unleashing the potential: transarterial chemoembolization combined with intra-arterial infusion of bevacizumab for unresectable hepatocellular carcinoma.

BACKGROUND: The purpose of this study is to compare the efficacy and safety of transarterial chemoembolization (TACE) alone with transarterial chemoembolization combined with the arterial infusion of bevacizumab (TACE + Bev) in patients with unresectable hepatocellular carcinoma (uHCC). METHODS: A retrospective analysis was conducted on 446 uHCC patients treated with TACE or TACE + Bev between January 2021 and March 2023. The study evaluated objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events in both treatment groups. RESULTS: Finally, the TACE group comprised 295 patients, and the TACE + Bev group comprised 151 patients. Patients in the TACE + Bev group exhibited significantly prolonged median PFS (7.9 months vs. 10.3 months, P = 0.013) and median OS (16.1 months vs. 21.4 months, P = 0.041), improved ORR (26.8% vs. 37.7%, P = 0.017) and DCR (71.5% vs. 80.8%, P = 0.033) compared to the TACE group. Multifactorial Cox analysis identified alpha-fetoprotein (AFP) > 400 ng/ml as an independent prognostic factor for PFS and OS. Meanwhile, portal vein cancer thrombosis and distant metastasis are poor prognostic factors for OS. The overall incidence of adverse events was similar between the two groups. CONCLUSION: In comparison with the TACE group, the TACE + Bev group demonstrated efficacy in improving outcomes for patients with uHCC with a manageable safety profile.

A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.

PURPOSE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. MATERIALS AND METHODS: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models. RESULTS: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. CONCLUSIONS: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.

The alterations in peripheral lymphocyte subsets predict the efficacy and prognosis of immune checkpoint inhibitors in hepatocellular carcinoma.

Background: Immune checkpoint inhibitor (ICI) treatments are promising therapies for hepatocellular carcinoma (HCC) patients. However, not all HCC patients benefit from immunotherapy. Therefore, it is urgent to explore markers for the clinical efficacy and prognosis of immunotherapy for liver cancer. This study aimed to investigate changes in peripheral blood lymphocyte subsets after immunotherapy and to assess their predictive and prognostic value. Methods: Sixty-one patients with advanced HCC were enrolled. Peripheral blood samples were collected from HCC patients before and after ICI treatment, and lymphocytes were detected by flow cytometry. The rank sum test, chi-square test, Kaplan‒Meier curve, and Cox regression model were used to determine the relationship between the changes in the percentages of peripheral blood lymphocyte subsets and clinicopathological characteristics, clinical efficacy, progression-free survival (PFS) and overall survival (OS). Results: After ICI treatment, the percentage of CD3+CD8+ T cells increased, and the percentage of B cells decreased. The changes in memory T cells percentages varied according to different immune efficacy groups. Age, history of hepatitis B infection, first-line therapy, and distant metastasis influenced the proportion of peripheral blood lymphocyte subsets in patients with advanced HCC. Furthermore, univariate analysis demonstrated that high percentage changes in the natural killer (NK) cells percentage change predicted longer PFS and OS. Conclusions: ICI treatment alters the percentage of peripheral blood lymphocyte subsets in immunotherapy-treated HCC patients. Changes in the proportion of lymphocyte subsets are influenced by variances in immunological response and clinicopathological features. A high degree of NK cells percentage change in HCC patients treated with ICI represents an independent prognostic predictor.

Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection.

PURPOSE: The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients. METHODS: We recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore. RESULTS: From January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7 months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5 cm, and alpha-fetoprotein (AFP) > 400 ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance. CONCLUSION: Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.