Hepatocellular carcinoma (HCC) is a complex malignancy with limited therapeutic options. Although the reduced efficacy of first-line treatment option sorafenib therapy has been largely attributed to autophagy in HCC cells, the specific pharmacological targets involved in this phenomenon remain unclear. We systematically evaluated seven small molecule autophagy inhibitors— SBP-7455, U0126, wortmannin, hydroxychloroquine, S130, SAR405, SP600125—each targeting distinct regulatory nodes within the autophagy pathway. The inhibitory concentrations of these compounds were determined, followed by combination treatments with sorafenib to assess additive or synergistic effects in three HCC cell lines (Huh7, HepG2, and Mahlavu) representing distinct HCC characteristics. Cell viability and migration were assessed in 2D cultures, while spheroid size was analyzed in 3D hanging drop models. Most inhibitors exhibited additive effects in 2D cultures; however, only U0126—a dual MEK1 and autophagy inhibitor—markedly enhanced sorafenib efficacy in 3D cultures across all cell lines. Combination treatments were also tested on sorafenib-resistant Huh7 cells, again revealing U0126 as the most effective synergistic inhibitor in this pre-established resistance model. Notably, the levels of HCC stemness and sorafenib resistance marker CD24 decreased when sorafenib was combined with U0126. These results highlight the potential importance of targeting autophagy-associated pathways and suggest that U0126 may enhance the efficacy of sorafenib in HCC. Further investigation of U0126’s dual action mechanism is warranted to optimize treatment strategies and develop newer drugs.
Colon cancer (CRC) prognosis is closely associated with metastasis, with circulating tumor cells (CTCs) displaying DNA methylation abnormalities linked to metastatic behavior. This study aims to develop a risk model for CRC by utilizing methylation-associated genes identified from CTCs to predict prognosis and identify candidate compounds for pharmacologic investigation. RNA-Seq data, clinical information, and DNA methylation levels were sourced from GEO and TCGA databases. Differential gene expression analysis and intersections led to the identification of methylation-driven differentially expressed genes (MD-DEGs). Prognosis-related MD-DEGs (PMD-DEGs) were identified and employed to construct a CRC risk model, validated through risk assessments, survival analyses, receiver operating characteristic (ROC) curve evaluations, independent prognostic analyses, and an external cohort (GSE39582). Differences in drug sensitivity between high- and low-risk patients were also assessed. RT-qPCR, western blotting, and immunohistochemical analyses validated the PMD-DEGs. A total of 69 MD-DEGs were identified in the CRC cohort, with a risk model constructed incorporating five PMD-DEGs: IDUA, ELF5, ZBTB18, NOVA1, and TCEAL3. In the training cohort, the model stratified CRC patients by prognosis, although external validation revealed only limited discriminative capability. Computational drug-sensitivity analysis revealed predicted associations between these five PMD-DEGs and the sensitivity to 13 anticancer compounds. Additionally, RT-qPCR, western blotting, and immunohistochemistry confirmed significant differences in theexpression of these genes between CRC tissues and para-cancer normal (PCN) samples, and highlightedcandidate compounds warranting future experimental investigation.
This work aimed to study the diagnostic value of quantitative parameters and serological biomarkers (SBs) in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for lymph node metastasis (LNM) in cervical cancer (CC), and construct a predictive model, this paper was conducted. Data of patients with pathologically confirmed CC were retrospectively analyzed. DCE-MRI quantitative parameters and serological indicators (SCC-Ag, VEGF, MMP-9) were collected. Build a nomogram prediction model and assess the quality of this model. Univariate regression analysis showed that Ktrans, Kep, Ve, SCC-Ag, VEGF, and MMP-9 were all significantly associated with LNM in CC (all P < 0.001). Multiple linear regression analysis showed that Ktrans, Kep, Ve, SCC-Ag and MMP-9 were independent predictors (all P < 0.001), but VEGF was not. The nomogram model in the development cohort had an AUC of 0.86 (95
Colorectal cancer (CRC) is the third most prevalent malignancy and the second leading cause of cancer-related mortality worldwide. Despite significant advancements in CRC screening, diagnosis, and therapeutic strategies in recent years, the long-term prognosis of patients remains severely compromised due to the tumor’s propensity for metastasis and recurrence. In this context, traditional Chinese medicine (TCM) provides a novel research direction for improving therapeutic outcomes in CRC patients, with its unique advantages of multi-target synergistic effects, low toxicity, and integrated prevention and treatment. This study applied a disease-gene-drug reverse network pharmacology workflow integrating public microarray datasets, CRC-associated gene databases, protein-protein interaction analysis, Comparative Toxicogenomics Database (CTD)-based compound mapping, traditional Chinese medicine (TCM) reverse annotation, independent clinical-cohort validation, and molecular docking. Through systematic analysis, 28 candidate herbs, 13 active compounds, and 3 core target molecules were identified; among these, 17 herbs (including Bajiaolian, Bohe, etc.) were reported for the first time to have potential therapeutic effects against CRC. Furthermore, molecular docking was used to explore the binding affinity and interaction modes of 9 active compounds with key targets. To the best of our knowledge, this study was the first to apply reverse network pharmacology for screening TCM with therapeutic potential against CRC, providing novel insights for both clinical management and theoretical research of CRC.
Renal cell carcinoma (RCC; KIRC) is one of the most common malignant tumors in the urinary system, with highly variable prognosis and the urgent need for reliable biomarkers. Necrosis by sodium overload (NECSO) is a novel form of cell death mediated by the TRPM4 channel, and its role in KIRC is unknown. This study aims to construct a prognostic model based on NECSO-related genes and explore its mechanism. We integrated KIRC data from public databases to analyze the functions of TRPM4 co-expressed genes. Using univariate and LASSO-Cox regression, we selected 12 key genes to construct a prognostic risk score model. The key gene RNF123 of the model was evaluated for its effects on the biological behaviors of KIRC cells through CCK8, EdU, apoptosis, cell scratch, Transwell experiments and subcutaneous tumorigenesis model. We constructed a NECSO-related prognostic risk model consisting of 12 characteristic genes. This model was able to effectively distinguish high-risk and low-risk patients in both the training set and validation set, and the risk score was an independent prognostic factor with stable predictive efficacy. The high-risk group was enriched in immune activation pathways but accompanied by inhibitory T cell infiltration, and was associated with high tumor mutation burden. Drug sensitivity analysis suggested that high-risk patients were more sensitive to TGFβR inhibitors. Additionally, the key gene RNF123 of prognostic model was lowly expressed in KIRC. Functional experiments indicated that RNF123 can inhibit cell proliferation and migration and promote apoptosis, and its mechanism may be related to upregulation of TRPM4 expression, a key mediator of NECSO, suggesting a potential link to enhanced sensitivity to NECSO. This study successfully constructed and validated a KIRC prognostic model based on NECSO, demonstrating good predictive value. It also revealed that RNF123 may inhibit KIRC progression in association with TRPM4‑mediated NECSO, providing new targets and ideas for prognostic assessment and targeted therapy.
