Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.

OBJECTIVES: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. METHODS: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). RESULTS: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. CONCLUSIONS: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. CLINICAL RELEVANCE STATEMENT: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. KEY POINTS: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Bacteria-Based Nanoprobes for Cancer Therapy.

Surgical removal together with chemotherapy and radiotherapy has used to be the pillars of cancer treatment. Although these traditional methods are still considered as the first-line or standard treatments, non-operative situation, systemic toxicity or resistance severely weakened the therapeutic effect. More recently, synthetic biological nanocarriers elicited substantial interest and exhibited promising potential for combating cancer. In particular, bacteria and their derivatives are omnipotent to realize intrinsic tumor targeting and inhibit tumor growth with anti-cancer agents secreted and immune response. They are frequently employed in synergistic bacteria-mediated anticancer treatments to strengthen the effectiveness of anti-cancer treatment. In this review, we elaborate on the development, mechanism and advantage of bacterial therapy against cancer and then systematically introduce the bacteria-based nanoprobes against cancer and the recent achievements in synergistic treatment strategies and clinical trials. We also discuss the advantages as well as the limitations of these bacteria-based nanoprobes, especially the questions that hinder their application in human, exhibiting this novel anti-cancer endeavor comprehensively.

Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.

PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. METHODS: 202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. RESULTS: The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. CONCLUSIONS: The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

Distinct MRI characteristics of spinal cord diffuse midline glioma, H3 K27-altered in comparison to spinal cord glioma without H3 K27-alteration and demyelination disorder.

BACKGROUND: An applicable magnetic resonance imaging (MRI) biomarker for diffuse midline glioma (DMG), H3 K27-altered of the spinal cord is important for non-invasive diagnosis. PURPOSE: To evaluate the efficacy of conventional MRI (cMRI) in distinguishing between DMGs, H3 K27-altered, gliomas without H3 K27-alteration, and demyelinating lesions in the spinal cord. MATERIAL AND METHODS: Between January 2017 and February 2023, patients with pathology-confirmed spinal cord gliomas (including ependymomas) with definite H3 K27 status and demyelinating diseases diagnosed by recognized criteria were recruited as the training set for this retrospective study. Morphologic parameter assessment was performed by two neuroradiologists on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted imaging. Variables with high inter- and intra-observer agreement were included in univariable correlation analysis and multivariable logistic regression. The performance of the final model was verified by internal and external testing sets. RESULTS: The training cohort included 21 patients with DMGs (13 men; mean age = 34.57 ± 13.489 years), 21 with wild-type gliomas (10 men; mean age = 46.76 ± 17.017 years), and 20 with demyelinating diseases (5 men; mean age = 49.50 ± 18.872 years). A significant difference was observed in MRI features, including cyst(s), hemorrhage, pial thickening with enhancement, and the maximum anteroposterior diameter of the spinal cord. The prediction model, integrating age, age2, and morphological characteristics, demonstrated good performance in the internal and external testing cohort (accuracy: 0.810 and 0.800, specificity: 0.810 and 0.720, sensitivity: 0.872 and 0.849, respectively). CONCLUSION: Based on cMRI, we developed a model with good performance for differentiating among DMGs, H3 K27-altered, wild-type glioma, and demyelinating lesions in the spinal cord.

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. METHODS: The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. RESULTS: The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. CONCLUSIONS: The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

miR-124 and VAMP3 Act Antagonistically in Human Neuroblastoma.

Neuroblastoma (NB) is the most common extracranial solid tumor that affects developing nerve cells in the fetus, infants, and children. miR-124 is a microRNA (miRNA) enriched in neuronal tissues, and VAMP3 (vesicle-associated membrane protein 3) has been reported to be an miR-124 target, although the relationship between NB and miR-124 or VAMP3 is unknown. Our current work identified that miR-124 levels are high in NB cases and that elevated miR-124 correlates with worse NB outcomes. Conversely, depressed VAMP3 correlates with worse NB outcomes. To investigate the mechanisms by which miR-124 and VAMP3 regulate NB, we altered miR-124 or VAMP3 expression in human NB cells and observed that increased miR-124 and reduced VAMP3 stimulated cell proliferation and suppressed apoptosis, while increased VAMP3 had the opposite effects. Genome-wide mRNA expression analyses identified gene and pathway changes which might explain the NB cell phenotypes. Together, our studies suggest that miR-124 and VAMP3 could be potential new markers of NB and targets of NB treatments.

Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images.

OBJECTIVES: Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS: In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS: The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS: Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT: Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS: • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.

Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.

OBJECTIVES: Edema is a complication of gamma knife radiosurgery (GKS) in meningioma patients that leads to a variety of consequences. The aim of this study is to construct radiomics-based machine learning models to predict post-GKS edema development. METHODS: In total, 445 meningioma patients who underwent GKS in our institution were enrolled and partitioned into training and internal validation datasets (8:2). A total of 150 cases from multicenter data were included as the external validation dataset. In each case, 1132 radiomics features were extracted from each pre-treatment MRI sequence (contrast-enhanced T1WI, T2WI, and ADC maps). Nine clinical features and eight semantic features were also generated. Nineteen random survival forest (RSF) and nineteen neural network (DeepSurv) models with different combinations of radiomics, clinical, and semantic features were developed with the training dataset, and evaluated with internal and external validation. A nomogram was derived from the model achieving the highest C-index in external validation. RESULTS: All the models were successfully validated on both validation datasets. The RSF model incorporating clinical, semantic, and ADC radiomics features achieved the best performance with a C-index of 0.861 (95% CI: 0.748-0.975) in internal validation, and 0.780 (95% CI: 0.673-0.887) in external validation. It stratifies high-risk and low-risk cases effectively. The nomogram based on the predicted risks provided personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration. CONCLUSION: This RSF model with a nomogram could represent a non-invasive and cost-effective tool to predict post-GKS edema risk, thus facilitating personalized decision-making in meningioma treatment. CLINICAL RELEVANCE STATEMENT: The RSF model with a nomogram built in this study represents a handy, non-invasive, and cost-effective tool for meningioma patients to assist in better counselling on the risks, appropriate individual treatment decisions, and customized follow-up plans. KEY POINTS: • Machine learning models were built to predict post-GKS edema in meningioma. The random survival forest model with clinical, semantic, and ADC radiomics features achieved excellent performance. • The nomogram based on the predicted risks provides personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration and shows the potential to assist in better counselling, appropriate treatment decisions, and customized follow-up plans. • Given the excellent performance and convenient acquisition of the conventional sequence, we envision that this non-invasive and cost-effective tool will facilitate personalized medicine in meningioma treatment.

Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features.

OBJECTIVE: Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features. METHODS: Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria. RESULTS: One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% ( P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set. CONCLUSIONS: Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.

Polymorphism of E6 and E7 Genes in Human Papillomavirus Types 31 and 33 in Northeast China.

Persistent infection with human papillomavirus (HPV) types 31 and 33 is an important causative factor for cervical cancer. The E6/E7 genes are key oncogenes involved in the immortalization and transformation of human epithelial cells. Genetic polymorphism may lead to differences in the virus' carcinogenic potential, the immune reaction of the host, and the potencies of vaccines. Few studies on HPV31/33 E6/E7 genetic polymorphism have been carried out. To study the genetic polymorphism of HPV31 and HPV33 E6/E7 genes in northeast China, these genes (HPV31 E6/E7, n = 151; HPV33 E6/E7, n = 136) were sequenced and compared to reference sequences (J04353.1, M12732.1) using BioEdit. Phylogenetic trees were constructed by the neighbor-joining method using MegaX. The diversity of the secondary structure was estimated using the PSIPred server. The positively selected sites were analyzed using PAML4.9. The major histocompatibility complex (MHC) class I and MHCII epitopes were predicted using the ProPred-I server and ProPredserver. B-cell epitopes were predicted using the ABCpred server. In the 151 HPV31E6 sequences, 25 (25/450) single-nucleotide mutations were found, 14 of which were synonymous mutations and 11 were nonsynonymous. In the 151 HPV31E7 sequences, 8 (8/297) nucleotide mutations were found, 3 of which were synonymous mutations and 5 were nonsynonymous. In the 136 HPV33E6 sequences, 17 (17/450) nucleotide mutations were observed, 7 of which were synonymous mutations and 10 were nonsynonymous. C14T/G (T5I/S) was a triallelic mutation. Finally, in the 136 HPV33E7 sequences, 9 (9/294) nucleotide mutations were observed, 3 of which were synonymous mutations and 6 were nonsynonymous. C134T/A (A45V/E) and C278G/A (T93S/N) were triallelic mutations. Lineage A was the most common lineage in both HPV31 and HPV33. In all of the sequences, we only identified one positively selected site, HPV33 E6 (K93N). Most nonsynonymous mutations were localized at sites belonging to MHC and/or B-cell predicted epitopes. Data obtained in this study should contribute to the development and application of detection probes, targeted drugs, and vaccines.

Sauchinone Inhibits the Proliferation and Immune Invasion Capacity of Colorectal Cancer Cells through the Suppression of PD-L1 and MMP2/MM9.

BACKGROUND: Colorectal cancer (CRC) is one of the most common tumors globally and a leading cause of cancer-related death. In China, CRC is the third most common cancer type. Sauchinone is known to exhibit anti-tumor and anti-inflammatory activity, but its effects on CRC have not been investigated to-date Objective: To investigate the effects of Sauchinone on CRC development and metastasis and its underlying mechanism( s) of action. METHODS: SW480 and HCT116 cells were treated with a range of concentrations of Sauchinone. Cell proliferation was measured using EDU assays and flow cytometry. RESULTS: Treatment with 50 µM Sauchinone decreased the expression of MMP2 and MMP9 and downregulated PD-L1 expression (PD-1/PD-L1) leading to checkpoint inhibition. Sauchinone treatment also enhanced the cytotoxicity of SW840 and HCT116 cells co-cultured with CD8+ T cells. The overexpression of PD-L1 rescued the anti-proliferative and cytotoxic effects of Sauchinone in both types. CONCLUSIONS: We show that Sauchinone suppresses CRC cell growth through the downregulation of MMP2 and MM9 expression and PD-1/PD-L1 mediated checkpoint inhibition. Collectively, these data highlight the promise of Sauchinone as a future anti-CRC therapeutic.

A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images.

BACKGROUND AND OBJECTIVE: Treatment for meningiomas usually includes surgical removal, radiation therapy, and chemotherapy. Accurate segmentation of tumors significantly facilitates complete surgical resection and precise radiotherapy, thereby improving patient survival. In this paper, a deep learning model is constructed for magnetic resonance T1-weighted Contrast Enhancement (T1CE) images to develop an automatic processing scheme for accurate tumor segmentation. METHODS: In this paper, a novel Convolutional Neural Network (CNN) model is proposed for the accurate meningioma segmentation in MR images. It can extract fused features in multi-scale receptive fields of the same feature map based on MR image characteristics of meningiomas. The attention mechanism is added as a helpful addition to the model to optimize the feature information transmission. RESULTS AND CONCLUSIONS: The results were evaluated on two internal testing sets and one external testing set. Mean Dice Similarity Coefficient (DSC) values of 0.886, 0.851, and 0.874 are demonstrated, respectively. In this paper, a deep learning approach is proposed to segment tumors in T1CE images. Multi-center testing sets validated the effectiveness and generalization of the method. The proposed model demonstrates state-of-the-art tumor segmentation performance.

Macrophage-Specific Cathepsin as a Marker Correlated with Prognosis and Tumor Microenvironmental Characteristics of Clear Cell Renal Cell Carcinoma.

Background: Cathepsin Z (CTSZ) is a cathepsin family member that plays a dual role in the adhesion and migration of immune and tumor cells. Methods: The expression pattern of CTSZ in clear cell renal cell carcinoma (ccRCC) was observed by immunohistochemistry and validated by using double-labeling immunofluorescence. Publicly available single-cell sequencing data was used to further define the cell type-specific CTSZ expression in ccRCC. Methylation modification, immune infiltration, and tumor-related signaling enrichment analyses involving CTSZ were performed using multi-omics data. Data from two independent cohorts of anti-programmed death-1 (PD-1) therapeutic clinical trials were used to investigate correlations between CTSZ levels and treatment responses. Results: CTSZ was upregulated in ccRCC tissues compared with adjacent normal tissues at the RNA but not in ccRCC cells. Immunohistochemistry indicated that CTSZ was expressed in tumors infiltrated with lymphocytes. Double immunofluorescence demonstrated that CTSZ was co-expressed with CD68 but not CD8. Single-cell transcriptome data showed macrophage-specific expression of CTSZ in ccRCC. High CTSZ expression was significantly correlated with the enrichment of interferon-γ, epithelial-to-mesenchymal transition, cell cycle, apoptosis pathways, and B cell, macrophage, neutrophil, and dendritic cell infiltrations, as well as the expression of immune checkpoints CTLA4, LAG3, HAVCR2, PDCD1LG2, PDCD1, TIGIT, and SIGLEC15. Hypomethylation modification of cg02744249, cg02744249, and cg22145559 were negatively correlated with CTSZ expression, suggesting an epigenetic mechanism for the regulation of CTSZ expression. Clinically, CTSZ levels were associated with the prognosis of patients with ccRCC (hazard ratio=1.5, P=0.007). Notably, patients with higher CTSZ expression had a worse prognosis with anti-PD-1 monotherapy (hazard ratio=1.51, P=0.039). Conclusion: Macrophage-specific CTSZ was associated with activation of epithelial-to-mesenchymal transition, cell cycle signatures, and a higher infiltration level of B cells, macrophages, neutrophils, and dendritic cells in the tumor microenvironment. High expression of CTSZ could be considered as a prognostic and treatment response biomarker for patients with ccRCC receiving anti-PD-1 immunotherapy.

Cuprotosis-related signature predicts overall survival in clear cell renal cell carcinoma.

Background: Cuprotosis is a new form of programmed cell death induced by copper. We explored the correlation of cuprotosis with clear cell renal cell carcinoma (ccRCC) and constructed a cuprotosis-related signature to predict the prognosis of patients with ccRCC. Methods: The clinical and transcriptomic data of ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA), cBioPortal, and GEO databases, and cuprotosis-related gene sets were contained in the previous study. A cuprotosis-related signature was developed based on data from TCGA and verified by data from cBioPortal and GEO databases. The immune cell infiltrates and the corresponding signature risk scores were investigated. Two independent cohorts of clinical trials were analyzed to explore the correlation of the signature risk score with immune therapy response. Results: A signature containing six cuprotosis-related genes was identified and can accurately predict the prognosis of ccRCC patients. Patients with downregulated copper-induced programmed death had a worse overall survival (hazard ratio: 1.90, 95% CI: 1.39-2.59, p < 0.001). The higher signature risk score was significantly associated with male gender (p = 0.026), higher tumor stage (p < 0.001), and higher histological grade (p < 0.001). Furthermore, the signature risk score was positively correlated with the infiltration of B cells, CD8+ T cells, NK cells, Tregs, and T cells, whereas it was negatively correlated with eosinophils, mast cells, and neutrophils. However, no correlation between cuprotosis and response to anti-PD-1 therapy was found. Conclusion: We established a cuprotosis signature, which can predict the prognosis of patients with ccRCC. Cuprotosis was significantly correlated with immune cell infiltrates in ccRCC.

Identification of an amino acid metabolism-associated gene signature predicting the prognosis and immune therapy response of clear cell renal cell carcinoma.

Background: The upregulation of amino acid metabolism is an essential form of metabolic reprogramming in cancer. Here, we developed an amino acid metabolism signature to predict prognosis and anti-PD-1 therapy response in clear cell renal cell carcinoma (ccRCC). Methods: According to the amino acid metabolism-associated gene sets contained in the Molecular Signature Database, consensus clustering was performed to divide patients into two clusters. An amino acid metabolism-associated signature was identified and verified. Immune cell infiltrates and their corresponding signature risk scores were investigated. Two independent cohorts of clinical trials were analyzed to explore the correspondence between the signature risk score and the immune therapy response. Results: Two clusters with different amino acid metabolic levels were identified by consensus clustering. The patients in the two clusters differed in overall survival, progression-free survival, amino acid metabolic status, and tumor microenvironment. We identified a signature containing eight amino acid metabolism-associated genes that could accurately predict the prognosis of patients with ccRCC. The signature risk score was positively correlated with infiltration of M1 macrophages, CD8+ T cells, and regulatory T cells, whereas it was negatively correlated with infiltration of neutrophils, NK cells, and CD4+ T cells. Patients with lower risk scores had better overall survival but worse responses to nivolumab. Conclusion: Amino acid metabolic status is closely correlated with tumor microenvironment, response to checkpoint blockade therapy, and prognosis in patients with ccRCC. The established amino acid metabolism-associated gene signature can predict both survival and anti-PD-1 therapy response in patients with ccRCC.

Evaluation of HIF-1α Expression in a Rat Glioma Model Using Intravoxel Incoherent Motion and R2* Mapping.

Accurate evaluation of HIF-1α levels can facilitate the detection of hypoxia niches in glioma and treatment decisions. To investigate the feasibility of intravoxel incoherent motion (IVIM) and R2* Mapping for detecting HIF-1α expression levels, sixteen rats with intracranial C6 gliomas were subjected to IVIM and R2* Mapping using a 7 Tesla MRI scanner. For each model, the brain tissue on the HIF-1α-stained slices was subdivided into multiple square regions of interest (ROIs) with areas of 1 mm2, for which HIF-1α expression was assessed by HALO software to form a maps of HIF scores with a 0-300 range. The IVIM and R2* Mapping images were processed to create maps of the D, D*, f and R2* that were then paired with the corresponding HIF score maps. The average D, D*, f, perfusion (f × D*) and R2* values were calculated for the ROIs in the tumor and normal brain regions with different HIF-1α levels and used in further analysis. In this study, the average tumor size of sixteen C6 model rats was 458 ± 46.52 mm3, and the 482 included ROIs consisted of 280 tumoral and 202 normal ROIs. The average HIF score for the tumor regions was significantly higher than normal brain tissue (p < 0.001), and higher HIF scores were obtained for the central part of tumors than peripheral parts (p=0.03). Compared with normal brain tissues, elevated perfusion and f values were observed in tumor regions (p = 0.021, 0.004). In tumoral ROIs, the R2* values were higher in the group with high HIF-1α expression than in the group with low HIF-1α expression (p = 0.003). A correlation analysis revealed a positive correlation between the R2* value and HIF scores (r = 0.43, p < 0.001) and a negative correlation between D* and the HIF scores (r = -0.30, p = 0.001). Discrepancies in HIF-1α expression were found among different intratumoral areas, and IVIM and R2* Mapping were found to be promising means of noninvasive detection of the distribution and expression level of HIF-1α.

A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients.

PURPOSE: This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features. MATERIALS AND METHODS: Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances. RESULTS: Patients with cortical involvement had shorter survival times in the training set (P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized. CONCLUSIONS: The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.

Reoperation for Recurrent Adrenocortical Carcinoma: A Systematic Review and Pooled Analysis of Population-Based Studies.

BACKGROUND: Adrenocortical carcinoma (ACC) is a rare neoplasm with a high recurrence rate. This study aimed to assess the role of surgery in the clinical management of recurrent ACC. METHODS: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched, and the hazard ratios were pooled. RESULTS: Patients who underwent resection for recurrence had significantly better OS or OS after recurrence than those who received only nonsurgical treatments (HR 0.34, p < 0.001). Prognostic factors were associated with decreased OS after recurrence, including multiple recurrence (HR 3.23, p = 0.001), shorter disease-free interval (HR 2.94, p < 0.001), stage III-IV of the original tumor (HR 6.17, p = 0.001), sex of male (HR 1.35, p = 0.04), and initial non-R0 resection (HR 2.13, p = 0.001). Prolonged OS after recurrence was observed in those who experienced incomplete resection (HR 0.43, 95% CI 0.31-0.52, I2 = 53%) compared with patients who only received nonsurgical treatments. In the reoperated group, patients who underwent complete resection of recurrence had a prolonged OS after recurrence compared with those who underwent incomplete resection (HR 0.23, p = 0.004). CONCLUSIONS: We confirmed the role of reoperation in the clinical management of recurrent ACC. Select patients might benefit from debulking surgery. The preoperative evaluation of the complete resection of the recurrence is the key means to decide whether patients should undergo surgery. Other prognostic factors associated with prolonged OS include single recurrence site, relatively longer disease-free interval, stage I-II of the original tumor, and female sex.

Integrated Analysis of Energy Metabolism Signature-Identified Distinct Subtypes of Bladder Urothelial Carcinoma.

Background: Bladder urothelial carcinoma (BLCA) is the most common type of bladder cancer. In this study, the correlation between the metabolic status and the outcome of patients with BLCA was evaluated using data from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Methods: The clinical and transcriptomic data of patients with BLCA were downloaded from the Cancer Genome Atlas and cBioPortal datasets, and energy metabolism-related gene sets were obtained from the Molecular Signature Database. A consensus clustering algorithm was then conducted to classify the patients into two clusters. Tumor prognosis, clinicopathological features, mutations, functional analysis, ferroptosis status analysis, immune infiltration, immune checkpoint-related gene expression level, chemotherapy resistance, and tumor stem cells were analyzed between clusters. An energy metabolism-related signature was further developed and verified using data from cBioPortal datasets. Results: Two clusters (C1 and C2) were identified using a consensus clustering algorithm based on an energy metabolism-related signature. The patients with subtype C1 had more metabolism-related pathways, different ferroptosis status, higher cancer stem cell scores, higher chemotherapy resistance, and better prognosis. Subtype C2 was characterized by an increased number of advanced BLCA cases and immune-related pathways. Higher immune and stromal scores were also observed for the C2 subtype. A signature containing 16 energy metabolism-related genes was then identified, which can accurately predict the prognosis of patients with BLCA. Conclusion: We found that the energy metabolism-associated subtypes of BLCA are closely related to the immune microenvironment, immune checkpoint-related gene expression, ferroptosis status, CSCs, chemotherapy resistance, prognosis, and progression of BLCA patients. The established energy metabolism-related gene signature was able to predict survival in patients with BLCA.