Nanoparticle-based X-ray radiosensitization strategies have garnered significant attention in recent years. However, the underlying mechanisms of radiosensitization remain incompletely understood. In this work, we explore the influence of the K-edge effect in the X-ray absorption of nanomaterials on sensitization. Due to the alignment of the K-edge of thulium (Tm) with the Bremsstrahlung peak in the energy spectrum of medical X-ray accelerators, the following four different rare-earth nanomaterials with varying Tm percentages were designed: NaTmF₄, NaTm_0.6Lu_0.4F₄, NaTm_0.4Lu_0.6F₄, and NaLuF₄. We evaluated the X-ray absorption and the ability to generate secondary electrons and reactive oxygen species (ROS) of these nanoparticles. The radiosensitizing effect was evaluated through clonogenic assays. Our results showed that the K-edge effect affected secondary electron generation but did not significantly change ROS production. Nonetheless, NaTmF₄ induced marginally more DNA damage in the U87 cells than the other cell types. NaTmF₄ also exhibited superior radiosensitization efficacy against the U87 tumor cells. This shows that secondary electrons and ROS play pivotal roles in radiosensitization, which might be crucial to improving cancer treatment efficacy through enhanced radiation therapy outcomes.

The ANLN gene encodes anillin, a protein that binds to actin. Recent research has identified ANLN’s function in the initiation and advancement of different cancers. However, its impact on gallbladder cancer (GBC) remains unexplored. This study aimed to elucidate its possible molecular mechanisms in GBC. ANLN expression was assessed using quantitative real-time polymerase chain reaction (QRT-PCR), Western blotting (WB), and immunohistochemistry (IHC), revealing elevated levels in GBC tissues. ANLN knockdown resulted in the inhibition of cell proliferation and migration, leading to apoptosis and cell cycle arrest. Conversely, ANLN overexpression had the opposite effects on GBC cells. In vivo experiments confirmed that ANLN knockdown inhibited GBC cell growth. RNA-seq and bioinformatics analysis revealed ANLN’s function in activating the PI3K/AKT signaling pathway. We further confirmed that ANLN could upregulate STRA6 expression, which activated PI3K/AKT signaling to enhance the growth and movement of GBC cells. These findings demonstrate ANLN’s involvement in GBC initiation and progression, suggesting its potential as a novel target for GBC.

Background: Long non-coding RNAs (lncRNAs) are crucial factors affecting the occurrence, progression, and prognosis of gastric carcinoma (GC). The accumulation of disulfide bonds to excessive levels in cells expressing high SLC7A11 triggers disulfidptosis, which functions as a regulated form of cellular death. Research has demonstrated that upregulated SLC7A11 is common in human cancers, but the effect of disulfidptosis on GC remains unclear. Identifying lncRNAs associated with disulfidptosis (drlncRNAs) and establishing a prognostic risk profile holds considerable importance for advancing GC research and treatment. Methods: Clinical records and transcriptomic datasets from individuals with GC were acquired from The Cancer Genome Atlas (TCGA) repository. A three-drlncRNA risk model was built using three common regression analysis methods. Then we used receiver operating characteristic (ROC) curves, independent prognostic analysis, and additional statistical approaches to assess the precision of the model. This investigation additionally encompassed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, immune cell infiltration evaluation, and pharmacological sensitivity predictions. To further investigate immunotherapy response disparities between patient cohorts with elevated- and reduced-risk scores, analyses of tumor mutational burden (TMB), tumor immune dysfunction and exclusion (TIDE), and microsatellite instability (MSI) were implemented. Results: We constructed a unique model composed of three drlncRNAs (AC107021.2, AC016394.2, and AC129507.1). Its independent prognostic capability for GC patients was validated through both single-variable and multivariable Cox regression analyses. GO and KEGG pathway assessments revealed predominant enrichment within the elevated-risk cohort, particularly in pathways involving sulfur compound interactions, traditional Wnt signaling mechanisms, cell-substrate adherens junctions, and cAMP signaling cascades, among others. Tumor microenvironment (TME) evaluation demonstrated elevated ImmuneScores, StromalScores, and ESTIMATEScores within the high-risk patient population. Concurrently, this elevated-risk cohort exhibited enhanced immune cell infiltration patterns, whereas the reduced-risk group displayed superior expression of immune checkpoints (ICPs). Additional investigations revealed that patients categorized into the reduced-risk classification possessed greater tumor mutational burden, increased MSI-high proportions, and diminished tumor immune dysfunction and exclusion scores compared to their high-risk counterparts. Pharmacological sensitivity assessments confirmed the superior efficacy of several therapeutic agents, including gemcitabine and veliparib (ABT.888), in patients with lower risk classifications. Conclusions: Our established risk stratification system demonstrates independent prognostic predictive capacity while offering personalized clinical intervention guidance for individuals diagnosed with GC.

Background: Long non-coding RNAs (lncRNAs) are crucial factors affecting the occurrence, progression and prognosis of gastric cancer. Disulfidptosis, a type of programmed cell death, results from abnormal accumulation of disulfide bonds in high-SLC7A11 cells. Research have demonstrated that upregulated SLC7A11 is common in human cancers, but the effect of disulfidptosis on gastric cancer remains unclear. It is of great significance to recognize disulfidptosis -related lncRNAs (drlncRNAs) and develop a risk signature with prognostic value in gastric cancer. Methods: The transcriptome data and clinical information of gastric cancer patients were obtained from TCGA (the Cancer Genome Atlas) database. A 3 drlncRNAs risk model was built by three common regression analysis methods. Then we used ROC curves, independent prognostic analysis and other methods to assess the accuracy of the model. Furthermore, GO and KEGG enrichment analysis, immune infiltration analysis and drug sensitivity prediction were also performed in this study. Finally, TMB, MSI, and TIDE analyses were conducted to further explore the difference of high- and low- risk score group in immunotherapy response. Results: We constructed a prognostic model composed of 3 drlncRNAs (AC107021.2, AC016394.2 and AC129507.1). Univariate and multivariate Cox regression proved that the model was able to predict the prognosis of GC patients independently. GO and KEGG analyses suggested that the high-risk group mainly enriched in sulfur compound binding, canonical WNT signaling pathway, cell-substrate adherens junction, cAMP signaling pathway and so on. TME analysis indicated that ImmuneScores, StromalScores, and ESTIMATEScores were higher in the high-risk group. Meanwhile, the high-risk group showed higher levels of immune cell infiltration, while the low-risk group exhibited higher expression levels of immune checkpoints. Our research further revealed that, compared to patients in the high-risk group, patients in the low-risk group had higher tumor mutation burden, a higher proportion of MSI-H, and lower TIDE scores. Finally, gemcitabine, ABT.888 (veliparib) and other sensitive drugs were confirmed to be more effective in low-risk groups. Conclusion: The risk model we constructed can independently predict prognosis and provide precise and individual clinical treatment guidance for patients with GC.