Ferroptosis sensitization in glioma: exploring the regulatory mechanism of SOAT1 and its therapeutic implications.

Glioma, the most common primary malignant tumor of the central nervous system, lacks effective targeted therapies. This study investigates the role of SOAT1, a key gene involved in cholesterol esterification, in glioma prognosis and its association with ferroptosis. Although the impact of SOAT1 on glioma prognosis has been recognized, its precise mechanism remains unclear. In this study, we demonstrate that inhibiting SOAT1 increases the sensitivity of glioma cells to ferroptosis, both in vitro and in vivo. Mechanistically, SOAT1 positively modulates the expression of SLC40A1, an iron transporter, resulting in enhanced intracellular iron outflow, reduced intracellular iron levels, and subsequent disruption of ferroptosis. Importantly, we find that SOAT1 regulates ferroptosis independently of SREBPs, which are known to be involved in ferroptosis regulation. Furthermore, we identify the involvement of the PI3K-AKT-mTOR signaling pathway in mediating the regulatory effects of SOAT1 on SLC40A1 expression and ferroptosis sensitivity. These findings highlight the contribution of intracellular signaling cascades in the modulation of ferroptosis by SOAT1. We show that inhibiting SOAT1 enhances the efficacy of radiotherapy in gliomas, both in vitro and in vivo, by promoting sensitivity to ferroptosis. This suggests that targeting SOAT1 could potentially improve therapeutic outcomes for glioma patients. In summary, this study uncovers the pivotal role of SOAT1 as a link between cholesterol esterification and ferroptosis in glioma. Our findings underscore the potential of SOAT1 as a promising clinical therapeutic target, providing new avenues for the development of effective treatments for glioma. Further research is warranted to unravel the complete regulatory mechanisms of SOAT1 and explore its clinical applications.

Utility of 18F-FDG uptake in predicting major pathological response to neoadjuvant immunotherapy in patients with resectable non­small cell lung cancer.

PURPOSE: The aim of present study was to investigate the efficiency of 18F-FDG uptake in predicting major pathological response (MPR) in resectable non-small cell lung cancer (NSCLC) patients with neoadjuvant immunotherapy. METHODS: A total of 104 patients with stage I-IIIB NSCLC were retrospectively derived from National Cancer Center of China, of which 36 cases received immune checkpoint inhibitors (ICIs) monotherapy (I-M) and 68 cases with ICI combination therapy (I-C). 18F-FDG PET-CT scans were performed at baseline and after neoadjuvant therapy (NAT). Receiver-operating characteristic (ROC) curve analyses were conducted and area under ROC curve (AUC) was calculated for biomarkers including maximum standardized uptake value (SUVmax), inflammatory biomarkers, tumor mutation burden (TMB), PD-L1 tumor proportion score (TPS) and iRECIST. RESULTS: Fifty-four resected NSCLC tumors achieved MPR (51.9%, 54/104). In both neoadjuvant I-M and I-C cohorts, post-NAT SUVmax and the percentage changes of SUVmax (ΔSUVmax%) were significantly lower in the patients with MPR versus non-MPR (p < 0.01), and were also negatively correlated with the degree of pathological regression (p < 0.01). The AUC of ΔSUVmax% for predicting MPR was respectively 1.00 (95% CI: 1.00-1.00) in neoadjuvant I-M cohort and 0.94 (95% CI: 0.86-1.00) in I-C cohort. Baseline SUVmax had a statistical prediction value for MPR only in I-M cohort, with an AUC up to 0.76 at the threshold of 17.0. ΔSUVmax% showed an obvious advantage in MPR prediction over inflammatory biomarkers, TMB, PD-L1 TPS and iRECIST. CONCLUSION: 18F-FDG uptake can predict MPR in NSCLC patients with neoadjuvant immunotherapy.

Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.

Background: Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DLIR) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V) in contrast-enhanced renal CT at different reconstruction strengths and doses. Methods: From January 2020 to May 2021, we prospectively included 101 patients who underwent renal contrast-enhanced CT scanning (69 at 120 kV; 32 at 80 kV). All image data were reconstructed with ASiR-V (30% and 70%) and DLIR at low, medium, and high reconstruction strengths (DLIR-L, DLIR-M, and DLIR-H, respectively). The CT number, noise, noise reduction rate (NRR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, and the proportion of acceptable images were compared. Lesions of DLIR groups were evaluated, and the conspicuity-to-noise ratio (C/N) was calculated. Results: Quantitative values (noise, SNR, CNR, and NRR) significantly differed between all reconstructions at 120 and 80 kV (P<0.001) and between each reconstruction, except ASiR-V 70% vs. DLIR-M. At 120 kV, the overall image quality and the proportion of acceptable images significantly differed between all reconstructions (P<0.001) and between each reconstruction, except ASiR-V 30% vs. DLIR-L and ASiR-V 70% vs. DLIR-M. At 80 kV, the overall image quality significantly differed between all reconstructions (P<0.001) and between each reconstruction, except between ASiR-V 30%, ASiR-V 70%, and DLIR-L. Quantitative and qualitative values were highest in DLIR-H, while the values were close in DLIR-H (80 kV) vs. ASiR-V 70% (120 kV) and DLIR-M (80 kV) vs. ASiR-V 30% (120 kV). The lesion conspicuity and noise significantly differed in DLIR at 120 kV and 80 kV (P<0.001). C/N significantly differed in DLIR at 120 kV (P<0.001) but not at 80 kV. DLIR-L and DLIR-M exhibited much-improved lesion display (C/N >1), and DLIR-H exhibited much-improved noise (C/N <1) at 120 kV. Conclusions: DLIR significantly improved the image quality and lesion visibility of renal CT compared with ASiR-V, even at a low dose.

MRI Findings in Trigeminal Neuralgia without Neurovascular Compression: Implications of Petrous Ridge and Trigeminal Nerve Angles.

OBJECTIVE: To determine the anatomical characteristics of the petrous ridge and trigeminal nerve in trigeminal neuralgia (TN) without neurovascular compression (NVC). MATERIALS AND METHODS: From May 2017 to March 2021, 66 patients (49 female and 17 male; mean age ± standard deviation [SD], 56.8 ± 13.3 years) with TN without NVC and 57 controls (46 female and 11 male; 52.0 ± 15.6 years) were enrolled. The angle of the petrous ridge (APR) and angle of the trigeminal nerve (ATN) were measured using magnetic resonance imaging with a high-resolution three-dimensional T2 sequence. Data on the symptomatic side were compared with those on the asymptomatic side in patients and with the mean measurements of the bilateral sides in controls. Receiver operating characteristic (ROC) analysis was conducted to evaluate the performance of APR and ATN in distinguishing TN patients from controls. RESULTS: In TN patients without NVC, the mean ± standard deviation (SD) of APR on the symptomatic side (98.40° ± 19.75°) was significantly smaller than that of the asymptomatic side (105.59° ± 22.45°, p = 0.019) and controls (108.44° ± 15.98°, p = 0.003). The mean ATN ± SD on the symptomatic side (144.41° ± 8.92°) was significantly smaller than that of the asymptomatic side (149.67° ± 8.09°, p = 0.003) and controls (150.45° ± 8.48°, p = 0.001). The area under the ROC curve for distinguishing TN patients from controls was 0.673 (95% confidence interval [CI]: 0.579-0.758) for APR and 0.700 (CI: 0.607-0.782) for ATN. The sensitivity and specificity using the diagnostic cutoff yielding the highest Youden index were 81.8% (54/66) and 49.1% (28/57), respectively, for APR (with a cutoff score of 94.30°) and 65.2% (43/66) and 66.7% (38/57), respectively, for ATN (cutoff score, 148.25°). CONCLUSION: In patients with TN without NVC, APR and ATN were smaller than those in controls, which may explain the potential cause of TN and provide additional information for diagnosis.

Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography.

OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V). METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable. RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively. CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.

Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose. METHODS: The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared. RESULTS: For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%). CONCLUSION: Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.

IDH1R132H Promotes Malignant Transformation of Benign Prostatic Epithelium by Dysregulating MicroRNAs: Involvement of IGF1R-AKT/STAT3 Signaling Pathway.

Risk stratification using molecular features could potentially help distinguish indolent from aggressive prostate cancer (PCa). Mutations in isocitrate dehydrogenase (IDH) acquire an abnormal enzymatic activity, resulting in the production of 2-hydroxyglutarate and alterations in cellular metabolism, histone modification, and DNA methylation. Mutant IDH1 has been identified in various human malignancies, and IDH1R132H constituted the vast majority of mutational events of IDH1. Most recent studies suggested that IDH1 mutations define a methylator subtype in PCa. However, the function of IDH1R132H in PCa development and progression is largely unknown. In this study, we showed that the prevalence of IDH1R132H in Chinese PCa patients is 0.6% (2/336). Of note, IDH1R132H-mutant PCa patients lacked other canonical genomic lesions (e.g., ERG rearrangement, PTEN deletion) that are common in most other PCa patients. The in vitro experiment suggested that IDH1R132H can promote proliferation of benign prostate epithelial cell RWPE-1 when under the situation of low cytokine. It could also promote migration capacity of RWPE-1 cells. Mechanistically, IDH1R132H was an important regulator of insulin-like growth factor 1receptor (IGF1R) by downregulating a set of microRNAs (miR-141-3p, miR-7-5p, miR-223-3p). These microRNAs were repressed by the alteration of epigenetic modification to decrease the enrichment of active marker H3K4me3 or to increase repressive marker H3K27me3 at their promoters. Collectively, we proposed a novel model for an IDH1R132H-microRNAs-IGF1R regulatory axis, which might provide insight into the function of IDH1R132H in PCa development.

Uhrf2 deletion impairs the formation of hippocampus-dependent memory by changing the structure of the dentate gyrus.

Ubiquitin-like with PHD and ring finger domains 2 (Uhrf2) is distributed in many brain regions, including the cortex and hippocampus. Decreased Uhrf2 expression is involved in neurodegenerative disease. A recent study showed Uhrf2 deletion impaired spatial memory; however, the mechanism remains elusive. In our study, we determined that Uhrf2+/- and Uhrf2-/- mice had significant learning and memory deficiencies in contextual fear conditioning (CFC) and the novel place recognition test but not in the novel object recognition test. Interestingly, there were no changes in the Uhrf2 protein levels in the hippocampus of C57BL6 mice after CFC training, which suggests Uhrf2 in adult mice may not be related to the formation of CFC long-term memory. Based on Nissl staining, Uhrf2 deletion caused neuropathological changes specifically in the crest of the dentate gyrus (DG), such as cell swelling, a vague outline and confused boundary; however, no changes were identified in the medial prefrontal cortex (mPFC). Transmission electron microscope assay further indicated a series of abnormal ultrastructure changes in neurons and glia in the DG crest. These results suggested that Uhrf2 deletion selectively blocked the development of the DG crest and impaired hippocampus-dependent learning and memory. Our study will facilitate a better understanding of the role of Uhrf2 protein in the central nervous system.