The comprehensive roles of lncRNA FAM99A/FAM99B in hepatocellular carcinoma: Expressions, regulatory mechanisms and functional pathway analysis.

AIMS: The incidence and mortality of liver hepatocellular carcinoma (LIHC) were increasing year by year. The aim of this study was to investigate the comprehensive roles of lncRNA FAM99A and FAM99B in LIHC. MAIN METHODS: According to the data of TCGA and GTEx, the expression levels of FAM99A and FAM99B in LIHC were evaluated, and the overall survival (OS), disease-free survival (DFS), immune cell infiltration and tumor stage were analyzed. The subcellular localization of FAM99A and FAM99B in various cancer cell lines was predicted by lncATLAS database. In addition, we also used ENCORI, KEGG, LinkedOmics, Metascape and other databases. It was verified by in vivo and in vitro experiments. KEY FINDINGS: Compared with adjacent normal tissues, FAM99A and FAM99B were down-regulated in LIHC tissues, and significantly correlated with immune cell infiltration. With the progression of tumor stage and grade, the expression of FAM99A and FAM99B showed a decreasing trend, and the prognosis of patients were also poor. In addition, the biological functions, signaling pathways and protein interactions of FAM99A and FAM99B in LIHC were enriched to study the potential molecular mechanisms. The overlapping RNA binding proteins (RBP) of FAM99A and FAM99B mainly included CSTF2T, BCCIP, RBFOX2 and SF3B4. Finally, experiments showed that overexpression of FAM99A attenuated the proliferation, invasion, colony formation and tumor growth of LIHC cells. SIGNIFICANCE: Taken together, the above studies demonstrated that FAM99A and FAM99B had an inhibitory effect on the progression of LIHC, which might be promising diagnostic biomarkers and therapeutic targets for LIHC patients.

Comprehensive analysis of IGF2BP3 with expression features, prognosis, immune modulation and stemness in hepatocellular carcinoma and pan-cancer.

Insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3) is a critical m6A reader. It encodes proteins that contain several KH domains, which are important in RNA binding, RNA synthesis and metabolism. Lots of researches have studied the malignant potential of m6A readers in tumors. However, the biological functional analysis of IGF2BP3 in hepatocellular carcinoma (HCC) and pan-cancer is not comprehensive. In this study, we used a bioinformatics approach to comprehensively analyze the significance of IGF2BP3 in HCC through analyzing its expression, mutation, prognosis, protein-protein interaction (PPI) network, functional enrichment, and the correlation with ferroptosis, stemness as well as immune modulation in HCC. IGF2BP3 presented a negative correlation with the ferroptosis molecule NFE2L2, and a positive correlation with the ferroptosis molecule SLC1A5 as well as the immune checkpoint HAVCR2. In addition, we also analyzed IGF2BP3 expression, prognosis and immune modulation in pan-cancer, revealing the prognostic value of IGF2BP3 in a variety of tumors. Finally, we verified the biological functions of IGF2BP3 in HCC through various experiments. The data showed that IGF2BP3 may enhance the proliferation, colony formation and invasion capacities of HCC cells, and IGF2BP3 is mainly positively correlated with the expression level of stemness marker SOX2. In conclusion, IGF2BP3 had a potential to be a new perspective biomarker in forecasting the immune response, ferroptosis, stemness and prognosis of HCC or even pan-cancer.

Understanding decision curve analysis in clinical prediction model research.

BACKGROUND: Many medical graduate students lack a thorough understanding of decision curve analysis (DCA), a valuable tool in clinical research for evaluating diagnostic models. METHODS: This article elucidates the concept and process of DCA through the lens of clinical research practices, exemplified by its application in diagnosing liver cancer using serum alpha-fetoprotein levels and radiomics indices. It covers the calculation of probability thresholds, computation of net benefits for each threshold, construction of decision curves, and comparison of decision curves from different models to identify the one offering the highest net benefit. RESULTS: The paper provides a detailed explanation of DCA, including the creation and comparison of decision curves, and discusses the relationship and differences between decision curves and receiver operating characteristic curves. It highlights the superiority of decision curves in supporting clinical decision-making processes. CONCLUSION: By clarifying the concept of DCA and highlighting its benefits in clinical decisionmaking, this article has improved researchers' comprehension of how DCA is applied and interpreted, thereby enhancing the quality of research in the medical field.

The expression profiles of signature genes from CD103+LAG3+ tumour-infiltrating lymphocyte subsets predict breast cancer survival.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response. METHODS: Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts. RESULTS: We identified two TIL subsets with high expression of CD103 and LAG3 (CD103+LAG3+), including a CD8+ T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103+LAG3+ TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy. CONCLUSIONS: Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy.

The combination of radiomics features and VASARI standard to predict glioma grade.

Background and Purpose: Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods. Materials and Methods: Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation. Results: Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966. Conclusion: The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

GO accelerate iron oxides formation and tetrabromobisphenol A removal enhancement in the GO loaded NZVI system.

Tetrabromobisphenol A (TBBPA) is an emerging persistent organic pollutant, which is very difficult to remove by common methods. In this study, the GO-load nanoscale zero-valent iron (NZVI/GO) was fabricated and optimized to improve the reaction rate and removal efficiency for TBBPA reliably and efficiently. The results showed that GO-load significantly reduced the self-aggregation of NZVI and the aggregate size decreased by 50.00% (1400-700 nm). Meanwhile, GO significantly improved the reaction rate kobs (1.11 ± 0.11 h-1) of TBBPA in the NZVI/GO system compared to the NZVI (0.40 ± 0.08 h-1) system, and this increment was more pronounced (177.5%) when the mass ratio of NZVI-to-GO reached 1.0 than other mass ratios. Furthermore, X-Ray Diffraction and X-ray photoelectron spectroscopy analysis suggested that the Fe2+ transformation was changed and enriched by the GO. Only magnetite (Fe3O4) was detected on the surface of NZVI, whereas the maghemite (γ-Fe2O3), hematite (α-Fe2O3), and Fe3O4 were detected on the interface of NZVI/GO, which further performed the complexation adsorption through the -OH of TBBPA. This specific complexation adsorption is another potential accelerated removal mechanism for TBBPA and intermediates within the NZVI/GO system. This research has put forward a new perspective for widening the application of TBBPA removal using the synergistic effect between GO and NZVI.

Systematic pan-cancer analysis identifies RALA as a tumor targeting immune therapeutic and prognostic marker.

Introduction: RALA is a member of the small GTPase Ras superfamily and has been shown to play a role in promoting cell proliferation and migration in most tumors, and increase the resistance of anticancer drugs such as imatinib and cisplatin. Although many literatures have studied the cancer-promoting mechanism of RALA, there is a lack of relevant pan-cancer analysis. Methods: This study systematically analyzed the differential expression and mutation of RALA in pan-cancer, including different tissues and cancer cell lines, and studied the prognosis and immune infiltration associated with RALA in various cancers. Next, based on the genes co-expressed with RALA in pan-cancer, we selected 241 genes with high correlation for enrichment analysis. In terms of pan-cancer, we also analyzed the protein-protein interaction pathway of RALA and the application of small molecule drug Guanosine-5'-Diphosphate. We screened hepatocellular cancer (HCC) to further study RALA. Results: The results indicated that RALA was highly expressed in most cancers. RALA was significantly correlated with the infiltration of B cells and macrophages, as well as the expression of immune checkpoint molecules such as CD274, CTLA4, HAVCR2 and LAG3, suggesting that RALA can be used as a kind of new pan-cancer immune marker. The main functions of 241 genes are mitosis and protein localization to nucleosome, which are related to cell cycle. For HCC, the results displayed that RALA was positively correlated with common intracellular signaling pathways such as angiogenesis and apoptosis. Discussion: In summary, RALA was closely related to the clinical prognosis and immune infiltration of various tumors, and RALA was expected to become a broad-spectrum molecular immune therapeutic target and prognostic marker for pan-cancer.

MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma.

Objective: To investigate the role of pre-treatment magnetic resonance imaging (MRI) radiomics for the preoperative prediction of lymph node (LN) metastasis in patients with hypopharyngeal squamous cell carcinoma (HPSCC). Methods: A total of 155 patients with HPSCC were eligibly enrolled from single institution. Radiomics features were extracted from contrast-enhanced axial T-1 weighted (CE-T1WI) sequence. The most relevant features of LN metastasis were selected by the least absolute shrinkage and selection operator (LASSO) method. Univariate and multivariate logistic regression analysis was adopted to determine the independent clinical risk factors. Three models were constructed to predict the LN metastasis status: one using radiomics only, one using clinical factors only, and the other one combined radiomics and clinical factors. Receiver operating characteristic (ROC) curves and calibration curve were used to evaluate the discrimination and the accuracy of the models, respectively. The performances were tested by an internal validation cohort (n=47). The clinical utility of the models was assessed by decision curve analysis. Results: The nomogram consisted of radiomics scores and the MRI-reported LN status showed satisfactory discrimination in the training and validation cohorts with AUCs of 0.906 (95% CI, 0.840 to 0.972) and 0.853 (95% CI, 0.739 to 0.966), respectively. The nomogram, i.e., the combined model, outperformed the radiomics and MRI-reported LN status in both discrimination and clinical usefulness. Conclusions: The MRI-based radiomics nomogram holds promise for individual and non-invasive prediction of LN metastasis in patients with HPSCC.

Application of high resolution computed tomography image assisted classification model of middle ear diseases based on 3D-convolutional neural network. / 基于3Då·ç§¯ç¥žç»ç½‘ç»œçš„ä¸­è€³ç–¾ç— é«˜åˆ†è¾¨çŽ‡CTå›¾åƒè¾ åŠ©åˆ†ç±»è¯Šæ–­æ¨¡åž‹çš„åº”ç”¨.

OBJECTIVES: Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) are the 2 most common chronic middle ear diseases. In the process of diagnosis and treatment, the 2 diseases are prone to misdiagnosis and missed diagnosis due to their similar clinical manifestations. High resolution computed tomography (HRCT) can clearly display the fine anatomical structure of the temporal bone, accurately reflect the middle ear lesions and the extent of the lesions, and has advantages in the differential diagnosis of chronic middle ear diseases. This study aims to develop a deep learning model for automatic information extraction and classification diagnosis of chronic middle ear diseases based on temporal bone HRCT image data to improve the classification and diagnosis efficiency of chronic middle ear diseases in clinical practice and reduce the occurrence of missed diagnosis and misdiagnosis. METHODS: The clinical records and temporal bone HRCT imaging data for patients with chronic middle ear diseases hospitalized in the Department of Otorhinolaryngology, Xiangya Hospital from January 2018 to October 2020 were retrospectively collected. The patient's medical records were independently reviewed by 2 experienced otorhinolaryngologist and the final diagnosis was reached a consensus. A total of 499 patients (998 ears) were enrolled in this study. The 998 ears were divided into 3 groups: an MEC group (108 ears), a CSOM group (622 ears), and a normal group (268 ears). The Gaussian noise with different variances was used to amplify the samples of the dataset to offset the imbalance in the number of samples between groups. The sample size of the amplified experimental dataset was 1 806 ears. In the study, 75% (1 355) samples were randomly selected for training, 10% (180) samples for validation, and the remaining 15% (271) samples for testing and evaluating the model performance. The overall design for the model was a serial structure, and the deep learning model with 3 different functions was set up. The first model was the regional recommendation network algorithm, which searched the middle ear image from the whole HRCT image, and then cut and saved the image. The second model was image contrast convolutional neural network (CNN) based on twin network structure, which searched the images matching the key layers of HRCT images from the cut images, and constructed 3D data blocks. The third model was based on 3D-CNN operation, which was used for the final classification and diagnosis of the 3D data block construction, and gave the final prediction probability. RESULTS: The special level search network based on twin network structure showed an average AUC of 0.939 on 10 special levels. The overall accuracy of the classification network based on 3D-CNN was 96.5%, the overall recall rate was 96.4%, and the average AUC under the 3 classifications was 0.983. The recall rates of CSOM cases and MEC cases were 93.7% and 97.4%, respectively. In the subsequent comparison experiments, the average accuracy of some classical CNN was 79.3%, and the average recall rate was 87.6%. The precision rate and the recall rate of the deep learning network constructed in this study were about 17.2% and 8.8% higher than those of the common CNN. CONCLUSIONS: The deep learning network model proposed in this study can automatically extract 3D data blocks containing middle ear features from the HRCT image data of patients' temporal bone, which can reduce the overall size of the data while preserve the relationship between corresponding images, and further use 3D-CNN for classification and diagnosis of CSOM and MEC. The design of this model is well fitting to the continuous characteristics of HRCT data, and the experimental results show high precision and adaptability, which is better than the current common CNN methods.

Mesenteric abnormalities play an important role in grading intestinal fibrosis in patients with Crohn's disease: a computed tomography and clinical marker-based nomogram.

Background: While the grading of intestinal fibrosis is closely related to the therapeutic strategy of patients with Crohn's disease (CD), it has not yet been well resolved. Mesenteric abnormalities are inextricably linked to intestinal fibrosis. Objectives: We aimed to establish an optimal model for assessing intestinal fibrosis using computed tomography enterography (CTE) and clinical markers. Design: A total of 174 patients with CD between January 2014 and June 2020 were included in this retrospective multicentre study. Methods: All patients underwent CTE within 3 months prior to surgery. Intestinal fibrosis was pathologically scored as non-mild or moderate-to-severe. Selected imaging of the intestinal walls and mesentery and/or clinical factors were used to develop the diagnostic models. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the discrimination performance of the models. A decision curve analysis was performed to evaluate the clinical usefulness of the models. Results: One-, two-, and three-variable models were identified as possible diagnostic models. Model 1 [mesenteric creeping fat index (MCFI)], Model 2 (mesenteric oedema and MCFI), and Model 3 (mesenteric oedema, MCFI, and disease duration) were established. The AUCs of Model 1 in training and test cohorts 1 and 2 were 0.799, 0.859, and 0.693, respectively; Model 2 was 0.851, 0.833, and 0.757, respectively; and Model 3 was 0.832, 0.821, and 0.850, respectively. We did not observe any significant difference in diagnostic performance between the training and total test cohorts in any model (all p > 0.05). The decision curves showed that Model 3 had the highest net clinical benefit in test cohort 2. The nomogram of this optimal model was constructed by considering the favourable and robust performance of Model 3. Conclusion: A nomogram integrating mesenteric abnormalities on CTE with a clinical marker was optimal for differentiating between non-mild and moderate-to-severe fibrosis in patients with CD.

The comprehensive expression and functional analysis of m6A modification "readers" in hepatocellular carcinoma.

N6-methyladenosine (m6A) modification regulators are essential for the diagnosis and treatment of various cancers. However, the comprehensive analysis about roles of m6A "readers" in hepatocellular carcinoma (HCC) remains unclear. UALCAN, GEPIA2, HPA, Kaplan Meier plotter, cBioPortal, STRING WebGestalt, Metascape and TIMER 2.0 database and Cytoscape software were used to comprehensively analyze the bioinformatic data. We found that m6A "readers" were upregulated at the mRNA level and protein level in HCC patients. Highly expressed YTHDF1, IGF2BP3 and NKAP were positively correlated with advanced HCC stage and had a poor prognosis in OS and PFS. The gene alterations of m6A "readers" happened frequently, and YTHDF3 had the highest mutation rate. The function of m6A "readers" on HCC may be closely correlated with splicing related proteins (including HNRNP family, SNRP family, and SR family), metabolic process, protein binding and RNA splicing related signaling pathways. Moreover, although the correlation of YTHDF3 and CD8+ T cell infiltration, and the correlation of IGF2BP3 and infiltration of mast cells and CAF are negative, most m6A "readers" had a positive correlation with immune cells (including CD8+ T cell, CD4+ T cell, Tregs, B cell, neutrophil, monocyte, macrophage, myeloid dendritic cell, nature killer cell, mast cell, and CAF). Macrophages, CD4+ T cell, Treg, B cell, monocyte, and myeloid dendritic cell had a positively strong correlation (Rho>0.4) with most m6A "readers" (such as YTHDC1, YTHDC2, YTHDF1, IGF2BP3, HNRNPA2B1 and HNRNPC). In conclusion, by comprehensive analysis of m6A "readers", we found that they were involved in the prognosis of HCC, and m6A "readers" might regulate the development and progression of HCC by participating in some metabolism-related and RNA splicing-related signaling pathways as well as immune cell infiltration.

Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.

OBJECTIVES: To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors. METHODS: In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists. RESULTS: Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%). CONCLUSION: This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI. KEY POINTS: • Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.

New insights into checkpoint inhibitor immunotherapy and its combined therapies in hepatocellular carcinoma: from mechanisms to clinical trials.

Hepatocellular carcinoma (HCC) is one of the most lethal tumors in China and worldwide, although first-line therapies for HCC, such as atezolizumab and bevacizumab, have been effective with good results, the researches on new therapies have attracted much attention. With the deepening research on tumor immunology, the role and operation mechanism of immune cells in the tumor microenvironment (TME) of HCC have been explained, such as programmed cell death protein 1 (PD-1) binding to ligand could cause T cell exhaustion and reduce IFN-γ T cell secretion, cytotoxic T lymphocyte 4 (CTLA-4) and CD28 mediate immunosuppression by competing for B7 protein and disrupting CD28 signal transduction pathway, which also lays the foundation for the development and application of more new immune checkpoint inhibitors (ICIs). The biological behavior of various immune checkpoints has been proved in HCC, such as PD-1, programmed cell death ligand 1 (PD-L1), CTLA-4 and so on, leading to a series of clinical trials. Currently, FDA approved nivolumab, pembrolizumab and nivolumab plus ipilimumab for the treatment of HCC. However, the treatment of ICI has the disadvantages of low response rate and many side effects, so the combination of ICIs and various other therapies (such as VEGF or VEGFR inhibition, neoadjuvant and adjuvant therapy, locoregional therapies) has been derived. Further studies on immune checkpoint mechanisms may reveal new therapeutic targets and new combination therapies in the future.

Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study.

Background and Purpose: Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT). Materials and Methods: Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort. Results: The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone. Conclusion: Our combined predictive model was robust in differentiating patients with and without response to nCRT.

The Dysregulation of SOX Family Correlates with DNA Methylation and Immune Microenvironment Characteristics to Predict Prognosis in Hepatocellular Carcinoma.

Background: Due to the molecular heterogeneity of hepatocellular carcinoma (HCC), majority of patients respond poorly among various of therapy. This study is aimed at conducting a comprehensive analysis about roles of SOX family in HCC for obtaining more therapeutic targets and biomarkers which may bring new ideas for the treatment of HCC. Methods: UALCAN, Kaplan Meier plotter, cBioPortal, STRING, WebGestalt, Metascape, TIMER 2.0, DiseaseMeth, MethSurv, HPA, CCLE database, and Cytoscape software were used to comprehensively analyze the bioinformatic data. Results: SOX2, SOX4, SOX8, SOX10, SOX11, SOX12, SOX17, and SOX18 were significantly differentially expressed in HCC and normal tissues and were valuable for the grade and survival of HCC patients. In addition, the gene alterations of SOX family happened frequently, and SOX4 and SOX17 had the highest mutation rate. The function of SOX family on HCC may be closely correlated with the regulation of angiogenesis-related signaling pathways. Moreover, SOX4, SOX8, SOX11, SOX12, SOX17, and SOX18 were correlation with 8 types of immune cells (including CD8+ T cell, CD4+ T cell, B cell, Tregs, neutrophil, macrophage, myeloid DC, and NK cell), and we found that most types of immune cells had a positive correlation with SOX family. Notably, CD4+ T cell and macrophage were positively related with all these SOX family. NK cells were negatively related with most SOX family genes. DNA methylation levels in promoter area of SOX2, SOX4, and SOX10 were lower in HCC than normal tissues, while SOX8, SOX11, SOX17, and SOX18 had higher DNA methylation levels than normal tissues. Moreover, higher DNA methylation level of SOX12 and SOX18 demonstrated worse survival rates in patients with HCC. Conclusion: SOX family genes could predict the prognosis of HCC. In addition, the regulation of angiogenesis-related signaling pathways may participate in the development of HCC. DNA methylation level and immune microenvironment characteristics (especially CD4+ T cell and macrophage immune cell infiltration) could be a novel insight for predicting prognosis in HCC.

Case Report: Dual-Energy Computed Tomography of Cardiac Changes in IgG4-Related Disease.

Background: Dual-energy computed tomography (DECT) is used in coronary plaque characterization, myocardial perfusion imaging, and pulmonary embolism diagnosis; however, there is no relevant research on DECT in IgG4-related diseases (IgG4-RD) involving the coronary artery. We are the first to report DECT findings of cardiac morphology and function in IgG4-RD. Patient Findings: Multimodality cardiovascular imaging from a 63-year-old male patient, who presented with IgG4-related pancreatitis, was analyzed. An iodine map and spectral curves were obtained from the DECT, which can help to distinguish between non-calcified plaques and IgG4 lesions of the coronary artery, noninvasive FFRCT (fractional flow reserve derived from coronary computed tomography angiography) and ECV (extracellular volume fraction) demonstrated myocardial ischemia and myocardial fibrosis, respectively. Conclusion: The DECT can detect coronary artery tumor-like lesions caused by IgG4-RD and simultaneously assess the morphological, functional, and histological characteristics of the myocardium. This may help to guide individualized and timely treatment and avoid potentially life-threatening complications.

An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study.

OBJECTIVE: To explore whether radiomics features extracted from pre-treatment magnetic resonance imaging (MRI) can predict the overall survival (OS) in patients with hypopharyngeal squamous cell carcinoma. METHODS: A total of 190 patients with hypopharyngeal squamous cell carcinoma were eligibly enrolled from two institutions. Radiomics features were extracted from contrast-enhanced axial T1-weighted (CE-T1WI) sequence. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomics score correlated with OS. Multivariate logistic regression analysis was applied to determine the independent risk factors, which was combined with radiomics score to build the final radiomics nomogram. RESULTS: A radiomics score with 6 CE-T1WI features for OS prediction was constructed and validated; its integration with specific clinicopathologic factors (N stage) showed a better prediction performance in the training, internal validation, and external validation cohorts (C-index 0.78, 0.75, and 0.75). Calibration curves determined a good agreement between the predicted and actual overall survival. CONCLUSIONS: The radiomics-clinical nomogram and radiomics score might be non-invasive and reliable methods for the risk stratification in patients with hypopharyngeal squamous cell carcinoma. KEY POINTS: • An MRI-based radiomics model was constructed to evaluate of OS in patients with hypopharyngeal squamous cell carcinoma. • A radiomics-clinical nomogram that combined radiomics features and clinical characteristics was established. • Multi-cohort study validated the predictive performance of the radiomics-clinical nomogram to stratify patients with high risk in clinical practice.

Paradoxical effects of DNA tumor virus oncogenes on epithelium-derived tumor cell fate during tumor progression and chemotherapy response.

Epstein-Barr virus (EBV) and human papillomavirus (HPV) infection is the risk factors for nasopharyngeal carcinoma and cervical carcinoma, respectively. However, clinical analyses demonstrate that EBV or HPV is associated with improved response of patients, although underlying mechanism remains unclear. Here, we reported that the oncoproteins of DNA viruses, such as LMP1 of EBV and E7 of HPV, inhibit PERK activity in cancer cells via the interaction of the viral oncoproteins with PERK through a conserved motif. Inhibition of PERK led to increased level of reactive oxygen species (ROS) that promoted tumor and enhanced the efficacy of chemotherapy in vivo. Consistently, disruption of viral oncoprotein-PERK interactions attenuated tumor growth and chemotherapy in both cancer cells and tumor-bearing mouse models. Our findings uncovered a paradoxical effect of DNA tumor virus oncoproteins on tumors and highlighted that targeting PERK might be an attractive strategy for the treatment of NPC and cervical carcinoma.

Magnetic resonance imaging-based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma.

BACKGROUND: To establish a radiomics nomogram for survival prediction and determine if genomic data were related to radiomics signature in advanced nasopharyngeal carcinoma (NPC). METHODS: Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI) in 316 patients. A progression-free survival (PFS) nomogram was developed and validated by the combination of the radiomics signature and clinicopathologic factors. Whole transcriptomics sequencing was performed in pretreatment tumor samples; correlation of gene expression and radiomics signature was further investigated. RESULTS: A 24-feature-combined radiomics signature was highly correlated with PFS; its integration with clinical predictors showed good prediction performance in the training and the validation cohort (C-index: 0.80 and 0.73). A significant correlation was observed between certain gene expression and Rad-score, especially the mRNA expression of CDKL2, PLIN5, and SPAG1. CONCLUSION: As a noninvasive method, the MRI-based radiomics signature might enable the pretreatment prediction of prognosis and gene expressions profile in advanced NPC.

The functional roles, cross-talk and clinical implications of m6A modification and circRNA in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. HCC has high rates of death and recurrence, as well as very low survival rates. N6-methyladenosine (m6A) is the most abundant modification in eukaryotic RNAs, and circRNAs are a class of circular noncoding RNAs that are generated by back-splicing and they modulate multiple functions in a variety of cellular processes. Although the carcinogenesis of HCC is complex, emerging evidence has indicated that m6A modification and circRNA play vital roles in HCC development and progression. However, the underlying mechanisms governing HCC, their cross-talk, and clinical implications have not been fully elucidated. Therefore, in this paper, we elucidated the biological functions and molecular mechanisms of m6A modification in the carcinogenesis of HCC by illustrating three different regulatory factors ("writer", "eraser", and "reader") of the m6A modification process. Additionally, we dissected the functional roles of circRNAs in various malignant behaviors of HCC, thereby contributing to HCC initiation, progression and relapse. Furthermore, we demonstrated the cross-talk and interplay between m6A modification and circRNA by revealing the effects of the collaboration of circRNA and m6A modification on HCC progression. Finally, we proposed the clinical potential and implications of m6A modifiers and circRNAs as diagnostic biomarkers and therapeutic targets for HCC diagnosis, treatment and prognosis evaluation.