Nanomaterials with diagnostic and therapeutic functions have exciting potential to reshape the landscape of precision medicine. Impressive progress has been made toward the design and production of innovative theranostic nanomaterials that improve disease care, motivated by their ability to simultaneously provide diagnostic information and therapeutic benefits. Herein, the state‐of‐the‐art theranostic semiconductor quantum dots (QDs) are summarized, and the diverse types of QDs designed for the diagnosis and treatment of different diseases are discussed. The opportunities and benefits of QDs are highlighted throughout using in vitro and in vivo examples aimed at addressing various clinical challenges, including cancer, vascular dysfunctions, microbial infections, and medical tattoos. Over the past several years, this area has experienced enormous growth, particularly in preclinical animal imaging and therapy, which has brought the field closer to reaching human patients. Unfortunately, several barriers to clinical translation remain. Therefore, in addition to summarizing the key results from previous in vivo studies, the lessons learned from these studies are synthesized, perspective on the future steps needed for both fundamental studies and the clinical translation of theranostic QD nanotechnology to inform future QD design is provided.

Introduction: This study aims to use machine learning to conduct in-depth analysis of key factors affecting the recurrence of HCC patients with high preoperative systemic immune-inflammation index (SII) levels after receiving ablation treatment, and based on this, construct a nomogram model for predicting recurrence-free survival (RFS) of patients.Methods: This study included clinical data of 505 HCC patients who underwent ablation therapy at Beijing You’an Hospital from January 2014 to January 2020, and accepted 65 HCC patients with high SII levels from Beijing Ditan Hospital as an external validation cohort. 505 patients from Beijing You’an Hospital were divided into low SII and high SII groups based on the optimal cutoff value of SII scores. The high SII group was further randomly divided into training and validation cohorts in a 7:3 ratio. eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox regression analysis, were used to explore the factors affecting the post-ablation RFS of HCC patients. Based on the identified key factors, a nomogram model were developed to predict RFS in HCC patients, and their performance were evaluated using the concordance index (C index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The optimal cutoff value for nomogram scores was used to divide patients into low- and high-risk groups, and the effectiveness of the model in risk stratification was evaluated using Kaplan-Meier (KM) survival curves.Results: This study confirmed that age, BCLC stage, tumor number, and GGT level were independent risk factors affecting RFS in HCC patients. Based on the selected risk factors, an RFS nomogram was successfully constructed. The C-index, ROC curve, calibration curve, and DCA curve each demonstrated the discrimination, accuracy, and decision-making utility of the nomogram, indicating that it has good predictive performance. KM curve revealed the nomogram could significantly differentiate patient populations with different recurrence risk.Conclusion: We developed a reliable nomogram that can accurately predict the 1-, 3-, and 5-year RFS for HCC patients with high SII levels following ablation therapy.

Background and aim: Sarcopenia has gained considerable attention in the context of hepatocellular carcinoma, as it has been correlated with a poorer prognosis among patients undergoing sorafenib or lenvatinib treatment for hepatocellular carcinoma (HCC). The clinical significance of sarcopenia in first-line advanced HCC patients treated with lenvatinib and programmed death-1 (PD-1) inhibitors needs to be clarified.Methods: Sarcopenia was diagnosed using CT (Computed tomography) or MRI (Magnetic Resonance Imaging), with the psoas muscle index (PMI) as the surrogate marker. Patients were grouped based on sarcopenia presences, and a comparative analysis examined characteristics, adverse events, and prognosis. The Cox regression analysis was applied to identify independent prognostic factors for survival, while nomograms were constructed to predict 1-year survival.Results: Among 180 patients, 46 had sarcopenia. Patients with baseline sarcopenia demonstrated significantly inferior median progression-free survival (mPFS) (3.0 vs. 8.3 months) and median overall survival (mOS) (7.3 vs. 21.6 months). The same results for mPFS (3.3 vs. 9.2 months) and mOS (9.4 vs. 24.2 months) were observed in patients who developed sarcopenia after treatment. Furthermore, significantly higher grade 3 or higher adverse events (AEs) (73.91% vs 41.79%, p<0.001) were recorded in the sarcopenia group compared to the non-sarcopenia group. In the multivariate analysis, distant metastasis, elevated PLR and CRP levels, and low PMI remained independent predictive factors for poor OS. Additionally, skeletal muscle loss remained a significant independent risk factor for PFS. We developed a nomogram incorporating these four indicators, which predicted 12-month survival with a C-index of 0.853 (95% CI, 0.791 – 0.915), aligning well with actual observations.Conclusion: The prognosis of patients with HCC and sarcopenia is significantly worse when treated with lenvatinib and PD-1 inhibitors. The combination regimen of lenvatinib plus PD-1 inhibitors should be cautiously recommended due to the inferior prognosis and higher AEs.

Background: The tumor microenvironment (TME) plays a critical role in the development, progression, and clinical outcomes of hepatocellular carcinoma (HCC). Despite the critical role of natural killer (NK) cells in tumor immunity, there is limited research on their status within the tumor microenvironment of HCC. In this study, single-cell RNA sequencing (scRNA-seq) analysis of HCC datasets was performed to identify potential biomarkers and investigate the involvement of natural killer (NK) cells in the TME.Methods: Single-cell RNA sequencing (scRNA-seq) data were extracted from the GSE149614 dataset and processed for quality control using the “Seurat” package. HCC subtypes from the TCGA dataset were classified through consensus clustering based on differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was employed to construct co-expression networks. Furthermore, univariate and multivariate Cox regression analyses were conducted to identify variables linked to overall survival. The single-sample gene set enrichment analysis (ssGSEA) was used to analyze immune cells and the screened genes.Result: A total of 715 DEGs from GSE149614 and 864 DEGs from TCGA were identified, with 25 overlapping DEGs found between the two datasets. A prognostic risk score model based on two genes was then established. Significant differences in immune cell infiltration were observed between high-risk and low-risk groups. Immunohistochemistry showed that HRG expression was decreased in HCC compared to normal tissues, whereas TUBA1B expression was elevated in HCC.Conclusion: Our study identified a two-gene prognostic signature based on NK cell markers and highlighted their role in the TME, which may offer novel insights in immunotherapy strategies. Additionally, we developed an accurate and reliable prognostic model, combining clinical factors to aid clinicians in decision-making.

Background: Currently, there are a few treatment options for unresectable hepatocellular carcinoma (HCC) after progression following sorafenib (SOR) therapy, but with limited benefit. The purpose of this study was to investigate the efficacy and safety of tyrosine kinase inhibitors (TKIs) combined with immune checkpoint inhibitors (ICIs) as second-line treatment. Methods: From May 2018 to May 2021, a total of 93 HCCs who failed SOR treatment were included in this study and divided into TKI group (n = 37) and TKI-ICI group (n = 56). Overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR) and safety were estimated among the two groups. In addition, univariate and multivariate Cox regression analyses were performed for OS and PFS to identify possible prognostic factors. Results: With a median follow-up time of 13.7 months, the median age of patients was 56 (range, 50–64) years and most were male. All of the patients were hepatitis virus-related HCC. Both median OS (7.63 months vs 19.23 months, P < 0.001) and median PFS (2.97 months vs 8.63 months, P < 0.001) were significantly improved in the TKI-ICI group compared to the TKI group. A significant increase in DCR was demonstrated in the TKI-ICI group compared to the TKI group (83.9% vs 45.9%, P = 0.0003), although no significant difference in ORR was reported (21.4% vs 8.1%, P = 0.1552). Multivariate Cox regression analysis of OS and PFS revealed that second-line regimen was an independent protective factor affecting death and progression in HCCs after SOR failure. In addition, Child–Pugh B7 was an independent risk factor of OS. Finally, there was no significant difference in the incidence of any grade or grade 3/4 adverse events (AEs) between the two groups, and no treatment-related deaths were observed. Conclusion: This real-world study suggests that the combination of TKIs and ICIs benefits more than mono-TKIs and is well tolerated in HCCs with hepatitis virus infection after SOR failure.

Background: Lenvatinib (LEN) is the recommended first‐line therapy for unresectable hepatocellular carcinoma (uHCC), but resistance frequently develops, and limited data exist on second‐line treatments. This study evaluated the efficacy and safety with a focus on the sorafenib (SOR) or regorafenib (REG)‐ based monotherapy or combination therapy in patients with uHCC after failure of first‐line LEN.Methods: Patients with first‐line LEN failure between May 2018 and December 2023 were retrospectively collected. Based on second‐line regimens, 70 patients were divided into two groups: the TKI group (n = 21) and the TKI‐ICI group (n = 49). Overall survival (OS) and progression‐free survival (PFS) were analyzed by Kaplan–Meier methods, and multivariate analysis was performed to identify prognostic factors.Results: In the TKI‐ICI group, median PFS was 5.27 months and median OS was 12.53 months. In the TKI group, median PFS was 3.10 months and median OS was 7.50 months. The objective response rate (ORR) was 19.1% in the TKI group and 16.3% in the TKI‐ICI group. The disease control rate (DCR) was 85.7% in the TKI‐ICI group and 61.9% in the TKI group. In the TKI‐ICI cohort, multivariable Cox analysis revealed the high albumin to neutrophil ratio index (ANRI) was an independent predictor for PFS, while alpha‐fetoprotein > 400 ng/mL was the independent predictor for OS. Safety profiles in both cohorts showed manageable toxicity, with no treatment‐related deaths.Conclusions: The combination of TKI and ICI presents a promising second‐line treatment option after LEN failure, regardless of the specific second‐line TKI used.