Yuehua Li

Aberrant expression of mitochondrial quality regulation genes (MQRGs) is intricately linked to mitochondrial dysfunction and the progression of hepatocellular carcinoma (HCC), highlighting the urgent need for reliable prognostic biomarkers. In this study, we aimed to identify differentially expressed MQRGs from 20 candidate genes by analyzing transcriptomic profiles and clinical records from the TCGA ( n = 371) and GEO ( n = 167) datasets. The identified prognostic MQRGs, along with associated subtype differentially expressed genes (DEGs, n = 156), underwent LASSO and multivariate Cox regression analyses to construct a risk model, which was subsequently validated through time‐dependent ROC analysis, Kaplan‐Meier curves, and in vitro RT‐qPCR. Our findings established a robust 4‐MQRG signature comprising ANXA10, BAMBI, AKR1B15, and SPINK1, which revealed that patients classified as high risk had significantly shorter overall survival compared to their low‐risk counterparts ( p < 0.001). The predictive accuracy of this signature was noteworthy, yielding 1‐, 3‐, and 5‐year AUCs of 0.725, 0.696, and 0.747 in the training cohort (n = 243) and 0.676, 0.627, and 0.592 in the testing cohort ( n = 242), respectively. Furthermore, high‐risk scores were associated with distinct immunosuppressive tumor microenvironments and varying sensitivity to systemic therapies. The dysregulated expression of the four genes was corroborated by RT‐qPCR and analysis of the HPA database. In conclusion, this validated MQRG‐based prognostic signature serves as an accurate tool for survival prediction and risk stratification, thus offering valuable biomarker support for personalized therapeutic approaches in HCC.

Vasculogenic mimicry (VM) in breast cancer is recognized as an important non-endothelium-dependent vascular mechanism that promotes tumor invasiveness, survival, and metastatic potential; however, effective and specific intervention strategies remain limited. In this study, we innovatively constructed a nanodelivery system based on hybrid membranes derived from bacterial outer membrane vesicles and breast cancer cell membranes, termed mTOMV, and loaded it with the natural flavonoid isoliquiritigenin (ISL) to enhance targeted antitumor activity. ISL@mTOMV nanoparticles exhibited a uniform spherical morphology, favorable stability, and good biocompatibility. In vitro experiments demonstrated that ISL@mTOMV significantly inhibited the proliferation, migration, and invasion of MDA-MB-231 and MCF-7 breast cancer cells, while promoting tumor cell apoptosis. Mechanistically, ISL@mTOMV markedly reduced the expression of ACLY, H3K27ac, and SMAD5 and suppressed the activity of the TGF-β/SMAD signaling pathway. In vivo experiments further confirmed that ISL@mTOMV inhibited ACLY-mediated H3K27 acetylation, attenuated SMAD5 transcriptional activation, and blocked TGF-β/SMAD signaling, thereby significantly suppressing VM formation in breast cancer. Collectively, this study proposes a highly biocompatible and tumor-targeted nanotherapeutic strategy, providing a new technical approach and potential translational value for VM intervention in breast cancer.

Controlling the size of lipid-based nanomedicines is crucial for the in vivo drug delivery efficiency. Many microfluidic devices have been devoted to optimizing liposome size by adjusting the total flow rate (TFR) and flow rate ratio (FRR). However, existing microfluidic devices have difficulty comprehensively evaluating the influence of lipid concentration and fluid parameters on liposome size. Here, we developed a cascaded and concentration-encoded microfluidic platform (CCEMP) for high-throughput evaluation of lipid concentration effects on liposome size. The CCEMP consists of a microfluidic concentration gradient generator (MCGG) and a microfluidic liposome generator (MLG), enabling parallel screening of six lipid concentrations with 6-fold higher throughput than single-channel devices. To validate the biological significance of precise size tuning, we synthesized various drug-loaded liposomes using the CCEMP. In vitro and in vivo evaluations revealed that the CCEMP-optimized small-size liposomes exhibited superior cellular uptake and tumor accumulation compared to larger counterparts. This modular microfluidic device provides outstanding flexibility for accelerating formulation development and tailoring liposomal properties for enhanced delivery efficiency and therapeutic capacity.

Background: Lung adenocarcinoma (LUAD) is recognized as the predominant subtype of non-small cell lung cancer (NSCLC), characterized by its aggressive behavior and notable capacity for metastasis. Recent studies have uncovered the significance of palmitoylation, a vital post-translational modification, in regulating biological functions such as cellular signaling and protein localization, suggesting its potential role in cancer biology. However, the implications of palmitoylation-associated long non-coding RNAs (lncRNAs) in LUAD pathology remain insufficiently characterized. Methods: Utilizing publicly available datasets, we conducted an extensive bioinformatics analysis to evaluate the expression and prognostic significance of palmitoylation-associated lncRNAs in LUAD. Our analysis identified 13 lncRNAs as potential prognostic biomarkers. Specifically, we sought to develop a prognostic model based on these lncRNAs to improve our understanding of their contribution to LUAD prognosis. The robustness of the prognostic model was subsequently verified through survival analysis and receiver operating characteristic (ROC) curve assessments. Results: This study established a prognostic risk model based on 13 palmitoylation-related lncRNAs, demonstrating excellent predictive performance, with area under the curve (AUC) values of 0.743, 0.724, and 0.748 for 1-year, 3-year, and 5-year overall survival, respectively. Multivariate Cox regression analysis indicated that the model’s risk score serves as an independent prognostic predictor, surpassing traditional clinicopathological features. Differential expression analysis and quantitative reverse transcription polymerase chain reaction (qRT-PCR) validation revealed significant downregulation of AL157895.1 in LUAD tissues and cell lines, whereas AL355472.3, SALRNA1, AL590666.4, AC026355.2, and LINC00862 were significantly upregulated. Importantly, low expression of AL157895.1 was strongly associated with poor patient prognosis. Conclusions: The findings underscore the potential of palmitoylation-associated lncRNAs as independent prognostic markers for patients with LUAD, paving the way for more personalized treatment approaches. Future studies should further explore their biological mechanisms.

Lymph nodes (LNs) are the most common and typically the earliest sites of metastasis in tumors. This study aims to establish a T-cell-based prognostic model, providing a critical foundation for evaluating the prognosis of patients with lymph node-metastatic lung adenocarcinoma (LUAD). Single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were utilized, combined with high-dimensional weighted gene co-expression network analysis (hdWGCNA), differential expression analysis, and Cox/LASSO regression, to construct a T-cell-related prognostic model incorporating key genes CD69, GOLGA8A, and IL16. Potential drug-gene interactions were identified through protein-protein interaction analysis, RNA-binding protein regulatory network analysis, drug regulation studies, and molecular docking. The expression of model genes was validated in lung adenocarcinoma cell lines using gene expression profiling, quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and multiplex immunofluorescence (mIF) assays. The established T-cell-associated prognostic model (CD69, GOLGA8A, and IL16) was significantly correlated with immune microenvironment characteristics, tumor mutational burden, and potential therapeutic responsiveness. The low-risk group exhibited a more favorable immune profile. The predictive map, constructed by integrating clinical variables, significantly improved the model's interpretability and utility for personalized survival prediction. Further molecular analysis identified multiple drug-gene interactions, providing novel insights into therapeutic strategies. Experimental validation confirmed the differential expression of model genes in tumor tissues. The T-cell marker gene-based prognostic risk model provides a foundation for evaluating the prognosis of lymph node-metastatic LUAD and shows potential for optimizing targeted therapy and immunotherapy strategies.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer, characterized by high incidence and poor prognosis. Mitochondrial l