Alterations of White Matter Connectivity in Adults with Essential Hypertension.

Purpose: To explore the topology of the white matter network in individuals with essential hypertension by graph theory. Patients and Methods: T1-weighted image and diffusion tensor imaging (DTI) data from 43 patients diagnosed with essential hypertension (EHT) and 33 individuals with normotension (healthy controls, HCs) were incorporated in this cross-sectional study. Furthermore, structural networks were constructed by graph theory to calculate whole brain network characteristics and intracerebral node characteristics. Results: Both EHT and HC groups displayed small-worldness in their structural networks. The area under the curve (AUC) of the small-worldness coefficient (σ) was higher in the EHT group compared to the HC group, whereas the AUC of assortativity was lower in the EHT group in contrast to the HC group. The nodal clustering coefficient (CP) and local efficiency (Eloc) of the EHT group decreased in the right dorsolateral superior frontal gyrus and the left medial superior frontal gyrus. These values increased in the left anterior cingulate and paracingulate gyrus. Furthermore, weight and body mass index (BMI) were positively correlated with σ. Conclusion: The EHT group showed brain network separation and integration dysfunction. Weight and BMI were positively correlated with σ. The data acquired in this investigation implied that altered structural connectivity in the prefrontal region may be a potential neuroimaging marker in EHT patients.

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease.

Coronary heart disease (CHD) is one of the leading causes of deaths globally. Identification of serum metabolic biomarkers for its early diagnosis is thus much desirable. Serum samples were collected from healthy controls (n = 86) and patients with CHD (n = 166) and subjected to untargeted and targeted metabolomics analyses. Subsequently, potential biomarkers were detected and screened, and a clinical model was developed for diagnosing CHD. Four dysregulated metabolites, namely PC(17:0/0:0), oxyneurine, acetylcarnitine, and isoundecylic acid, were identified. Isoundecylic acid was not found in Human Metabolome Database, so we could not validate differences in its relative abundance levels. Further, the clinical model combining serum oxyneurine, triglyceride, and weight was found to be more robust than that based on PC(17:0/0:0), oxyneurine, and acetylcarnitine (AUC = 0.731 vs. 0.579, sensitivity = 83.0 vs. 75.5%, and specificity = 64.0 vs. 46.5%). Our findings indicated that serum metabolomics is an effective method to identify differential metabolites and that serum oxyneurine, triglyceride, and weight appear to be promising biomarkers for the early diagnosis of CHD.

Pan-Cancer Analysis of Genomic and Prognostic Characteristics Associated With Coronavirus Disease 2019 Regulators.

Background: Cancer patients are alleged to have poor coronavirus disease 2019 (COVID-19) outcomes. However, no systematic or comprehensive analyses of the role and mechanisms of COVID-19 receptor-related regulators in cancer are available. Methods: We comprehensively evaluated the genomic alterations and their clinical relevance of six COVID-19 receptor-related regulators [transmembrane serine protease 2 (TMPRSS2), angiotensinogen (AGT), angiotensin-converting enzyme 1 (ACE1), solute carrier family 6 member 19 (SLC6A19), angiotensin-converting enzyme 2 (ACE2), and angiotensin II receptor type 2 (AGTR2)] across a broad spectrum of solid tumors. RNA-seq data, single nucleotide variation data, copy number variation data, methylation data, and miRNA-mRNA interaction network data from The Cancer Genome Atlas (TCGA) of 33 solid tumors were analyzed. We assessed the sensitivities of drugs targeting COVID-19 receptor-related regulators, using information from the Cancer Therapeutics Response Portal database. Results: We found that there are widespread genetic alterations of COVID-19 regulators and that their expression levels were significantly correlated with the activity of cancer hallmark-related pathways. Moreover, COVID-19 receptor-related regulators may be used as prognostic biomarkers. By mining the genomics of drug sensitivities in cancer databases, we discovered a number of potential drugs that may target COVID-19 receptor-related regulators. Conclusion: This study revealed the genomic alterations and clinical characteristics of COVID-19 receptor-related regulators across 33 cancers, which may clarify the potential mechanism between COVID-19 receptor-related regulators and tumorigenesis and provide a novel approach for cancer treatments.

Acute abdominal pain induced by renal leiomyoma in a young patient: a case report.

Renal leiomyoma is a rare benign mesenchymal tumor of the kidney that predominantly originates from the renal capsule or pelvis. However, because of its nonspecific clinical and imaging features, renal leiomyoma remains poorly characterized and may even lead to radical or partial nephrectomy on the basis of preoperative suspicion of renal carcinoma. We herein present a case involving a 12-year-old boy with acute abdominal pain who was diagnosed with renal leiomyoma based on both clinical imaging and histopathological examination. One year after radical nephrectomy, the patient recovered to good condition. This case demonstrates that the comprehensive application of imaging and histology are essential for early clinical diagnosis and effective treatment of renal leiomyoma.

Pan-cancer analyses reveal genomics and clinical characteristics of the melatonergic regulators in cancer.

Melatonin, an endogenous hormone, plays protective roles in cancer. In addition to regulating circadian rhythms, sleep, and neuroendocrine activity, melatonin functions in various survival pathways. However, the mechanisms of melatonin regulation in cancer remain unknown. In the present study, we performed a comprehensive characterization of melatonin regulators in 9125 tumor samples across 33 cancer types using multi-omic data from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia. In the genomic landscape, we identified the heterozygous amplification of AANAT and GPR50, and heterozygous deletion of PER3, CYP2C19, and MTNR1A as the dominant alteration events. Expression analysis revealed methylation-mediated downregulation of melatonergic regulator expression. In addition, we found that melatonergic regulator expression could be used to predict patient survival in various cancers. In depth, microRNA (miRNA) analysis revealed an miRNA-mRNA interaction network, and the deregulated miRNAs were involved in melatonin secretion and metabolism by targeting circadian clock genes. Pathway analysis showed that melatonergic regulators were associated with inhibition of apoptosis, the cell cycle, the DNA damage response, and activation of RAS/MAPK and RTK signaling pathways. Importantly, by mining the Genomics of Drug Sensitivity in Cancer database, we discovered a number of potential drugs that might target melatonergic regulators. In summary, this study revealed the genomic alteration and clinical characteristics of melatonergic regulators across 33 cancers, which might clarify the relationship between melatonin and tumorigenesis. Our findings also might provide a novel approach for the clinical treatment of cancers.

Molecular Characterization and Clinical Relevance of Lysine Acetylation Regulators in Urological Cancers.

BACKGROUND: Lysine acetylation and deacetylation are posttranslational modifications that are able to link extracellular signals to intracellular responses. However, knowledge regarding the status of lysine regulators in urological cancers is still unknown. METHODS: We first systematically analyzed the genetic and expression alterations of 31 lysine acetylation regulators in urological cancers. The correlation between lysine acetylation regulators and activation of cancer pathways was explored. The clinical relevance of lysine acetylation regulators was further analyzed. RESULTS: We identified that there are widespread genetic alterations of lysine acetylation regulators, and that their expression levels are significantly associated with the activity of cancer hallmark-related pathways. Moreover, lysine acetylation regulators were found to be potentially useful for prognostic stratification. HDAC11 may act as a potential oncogene in cell cycle and oxidative phosphorylation of urological cancers. CONCLUSION: Lysine acetylation regulators are involved in tumorigenesis and progression. Our results provide a valuable resource that will guide both mechanistic and therapeutic analyses of the role of lysine acetylation regulators in urological cancers.

Comprehensive characterization of the tumor microenvironment for assessing immunotherapy outcome in patients with head and neck squamous cell carcinoma.

The tumor microenvironment (TME) constitutes a complex milieu of cells and cytokines that maintain equilibrium between tumor progression and prognosis. However, comprehensive analysis of the TME and its clinical significance in head and neck squamous cell carcinoma (HNSCC) remains to be unreported. In this study, based on large-scale RNA sequencing data pertaining to single nucleotide variants (SNVs) and copy number variations (CNVs) in HNSCC patients from The Cancer Genome Atlas database, we analysed subpopulations of infiltrating immune cells and evaluated the role of TME infiltration pattern (TME score) in assessing immunotherapy outcome. TME signature genes involved in several inflammation and immunity signalling pathways were observed in the TME score subtype, which were considered immunosuppressive and potentially responsible for significantly worse prognosis. In comparison with SNV- and CNV-mediated tumor mutation burden, TME score can significantly differentiate between high- and low-risk HNSCC and predict immunotherapy outcome. Our data provide clarity on the comprehensive landscape of interactions between clinical characteristics of HNSCC and tumor-infiltrating immune cells. TME score seems to be a useful biomarker that can predict immunotherapy outcome in HNSCC patients.

Radiation-induced occult insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The nomogram based on quantitative apparent diffusion coefficients for discrimination.

BACKGROUND: Radiation-induced insufficiency fractures (IF) is frequently occult without fracture line, which may be mistaken as metastasis. Quantitative apparent diffusion coefficient (ADC) shows potential value for characterization of benign and malignant bone marrow diseases. The purpose of this study was to develop a nomogram based on multi-parametric ADCs in the differntiation of occult IF from bone metastasis after radiotherapy (RT) for cervical cancer. METHODS: This study included forty-seven patients with cervical cancer that showed emerging new bone lesions in RT field during the follow-up. Multi-parametric quantitative ADC values were measured for each lesion by manually setting region of interests (ROIs) on ADC maps, and the ROIs were copied to adjacent normal muscle and bone marrow. Six parameters were calculated, including ADCmean, ADCmin, ADCmax, ADCstd, ADCmean ratio (lesion/normal bone) and ADCmean ratio (lesion/muscle). For univariate analysis, receiver operating characteristic curve (ROC) analysis was performed to assess the performance. For combined diagnosis, a nomogram model was developed by using a multivariate logistic regression analysis. RESULTS: A total of 75 bone lesions were identified, including 48 occult IFs and 27 bone metastases. There were significant differences in the six ADC parameters between occult IFs and bone metastases (p < 0.05), the ADC ratio (lesion/ muscle) showed an optimal diagnostic efficacy, with an area under ROC (AUC) of 0.887, the sensitivity of 95.8%, the specificity of 81.5%, respectively. Regarding combined diagnosis, ADCstd and ADCmean ratio (lesion/muscle) were identified as independent factors and were selected to generate a nomogram model. The nomogram model showed a better performance, yielded an AUC of 0.92, the sensitivity of 91.7%, the specificity of 96.3%, positive predictive value (PPV) of 97.8% and negative predictive value (NPV) of 86.7%, respectively. CONCLUSIONS: Multi-parametric ADC values demonstrate potential value for differentiating occult IFs from bone metastasis, a nomogram based on the combination of ADCstd and ADCmean ratio (lesion/muscle) may provide an improved classification performance.

Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization.

BACKGROUND: To develop and validate an MRI-based radiomics nomogram for differentiation of cervical spine ORN from metastasis after radiotherapy (RT) in nasopharyngeal carcinoma (NPC). METHODS: A radiomics nomogram was developed in a training set that comprised 46 NPC patients after RT with 95 cervical spine lesions (ORN, n = 51; metastasis, n = 44), and data were gathered from January 2008 to December 2012. 279 radiomics features were extracted from the axial contrast-enhanced T1-weighted image (CE-T1WI). A radiomics signature was created by using the least absolute shrinkage and selection operator (LASSO) algorithm. A nomogram model was developed based on the radiomics scores. The performance of the nomogram was determined in terms of its discrimination, calibration, and clinical utility. An independent validation set contained 25 consecutive patients with 47 lesions (ORN, n = 25; metastasis, n = 22) from January 2013 to December 2015. RESULTS: The radiomics signature that comprised eight selected features was significantly associated with the differentiation of cervical spine ORN and metastasis. The nomogram model demonstrated good calibration and discrimination in the training set [AUC, 0.725; 95% confidence interval (CI), 0.622-0.828] and the validation set (AUC, 0.720; 95% CI, 0.573-0.867). The decision curve analysis indicated that the radiomics nomogram was clinically useful. CONCLUSIONS: MRI-based radiomics nomogram shows potential value to differentiate cervical spine ORN from metastasis after RT in NPC.

The Serum Metabolic Biomarkers in Early Diagnosis and Risk Stratification of Acute Coronary Syndrome.

Despite advances in the treatment of coronary diseases, acute coronary syndrome (ACS) remains the leading cause of death worldwide. ACS is associated with metabolic abnormalities of lipid oxidation stress. In this study, based on liquid chromatograph mass spectrometry technique, we conducted the metabolic profiling analysis of serum samples from stable plaques (SPs) and vulnerable plaques (VPs) in ACS patients for exploring the potential biomarkers of plaque stability. The results showed that four differential metabolites were identified between the SPs and VPs, including betaine, acetylcarnitine, 1-heptadecanoyl-sn-glycero-3-phosphocholine, and isoundecylic acid. Meanwhile, the diagnostic model was identified using stepwise logistic regression and internally validated with 10-fold cross-validation. We analyzed the correlations between serum metabolic perturbations and plaque stability, and the serum betaine and ejection fraction-based model was established with a good diagnostic efficacy [area under the curve (AUC) = 0.808, sensitivity = 70.6%, and specificity = 80.0%]. In summary, we firstly illustrate the comprehensive serum metabolic profiles in ACS patients, suggesting that the combined model of serum betaine and ejection fraction seems to be used as the potential diagnostic biomarker for the vulnerability of plaque stability.

A key genomic signature associated with lymphovascular invasion in head and neck squamous cell carcinoma.

BACKGROUND: Lymphovascular invasion (LOI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), is predictive of poor survival; however, the associated clinical characteristics and underlying molecular mechanisms remain largely unknown. METHODS: We performed weighted gene co-expression network analysis to construct gene co-expression networks and investigate the relationship between key modules and the LOI clinical phenotype. Functional enrichment and KEGG pathway analyses were performed with differentially expressed genes. A protein-protein interaction network was constructed using Cytoscape, and module analysis was performed using MCODE. Prognostic value, expression analysis, and survival analysis were conducted using hub genes; GEPIA and the Human Protein Atlas database were used to determine the mRNA and protein expression levels of hub genes, respectively. Multivariable Cox regression analysis was used to establish a prognostic risk formula and the areas under the receiver operating characteristic curve (AUCs) were used to evaluate prediction efficiency. Finally, potential small molecular agents that could target LOI were identified with DrugBank. RESULTS: Ten co-expression modules in two key modules (turquoise and pink) associated with LOI were identified. Functional enrichment and KEGG pathway analysis revealed that turquoise and pink modules played significant roles in HNSCC progression. Seven hub genes (CNFN, KIF18B, KIF23, PRC1, CCNA2, DEPDC1, and TTK) in the two modules were identified and validated by survival and expression analyses, and the following prognostic risk formula was established: [risk score = EXPDEPDC1 * 0.32636 + EXPCNFN * (- 0.07544)]. The low-risk group showed better overall survival than the high-risk group (P < 0.0001), and the AUCs for 1-, 3-, and 5-year overall survival were 0.582, 0.634, and 0.636, respectively. Eight small molecular agents, namely XL844, AT7519, AT9283, alvocidib, nelarabine, benzamidine, L-glutamine, and zinc, were identified as novel candidates for controlling LOI in HNSCC (P < 0.05). CONCLUSIONS: The two-mRNA signature (CNFN and DEPDC1) could serve as an independent biomarker to predict LOI risk and provide new insights into the mechanisms underlying LOI in HNSCC. In addition, the small molecular agents appear promising for LOI treatment.

Melatonin reverses nasopharyngeal carcinoma cisplatin chemoresistance by inhibiting the Wnt/ß-catenin signaling pathway.

Cisplatin (DDP)-based concurrent chemo-radiotherapy is a standard approach to treat locoregionally advanced nasopharyngeal carcinoma (NPC). However, many patients eventually develop recurrence and/or distant metastasis due to chemoresistance. In this study, we aimed to elucidate the effects of melatonin on DDP chemoresistance in NPC cell lines in vitro and vivo, and we explored potential chemoresistance mechanisms. We found that DDP chemoresistance in NPC cells is mediated through the Wnt/ß-catenin signaling pathway. Melatonin not only reversed DDP chemoresistance, but also enhanced DDP antitumor activity by suppressing the nuclear translocation of ß-catenin, and reducing expression of Wnt/ß-catenin response genes in NPC cells. In vivo, combined treatment with DDP and melatonin reduced tumor burden to a greater extent than single drug-treatments in an orthotopic xenograft mouse model. Our findings provide novel evidence that melatonin inhibits the Wnt/ß-catenin pathway in NPC, and suggest that melatonin could be applied in combination with DDP to treat NPC.

Differential Expression and Alternative Splicing of Transcripts Associated With Cisplatin-Induced Chemoresistance in Nasopharyngeal Carcinoma.

Radiotherapy and adjuvant cisplatin (DDP) chemotherapy are standard administrations applied to treat nasopharyngeal carcinoma (NPC). However, the molecular changes and functions of DDP in NPC chemo-resistance remain poorly understood. In the present study, transcriptomic sequencing between 5-8F and 5-8F/DDP cells was performed to identify differential expression and alternative splicing (AS) characteristics in DDP-resistant NPC cells. Transcriptomic profiling identified 1,757 upregulated genes and 1,473 downregulated differentially expressed genes (DEGs). Bioinformatic analysis revealed that these DEGs were associated with or participated in important biological regulatory functions in NPC. Validation of 20 significant DEGs using quantitative real-time reverse transcription PCR showed that the expression patterns of 17 mRNAs were in accordance with the sequencing data. Intron retention was identified as the major AS event in chemoresistant cells. Furthermore, the expression level of matrix metalloproteinase 1 (MMP1), which was one of the most upregulated mRNAs in the chemoresistant cell lines, was significantly associated with the migration, invasion, and proliferation of NPC cells in vitro. Our study revealed that dysregulated genes and AS-mediated DDP chemoresistance might play important roles in NPC development and progression. Targeting aberrantly expressed genes might clarify the pathogenesis of NPC and contribute to developing new therapeutic strategies for NPC.

Corrigendum: The Tumor Suppressor Role of Zinc Finger Protein 671 (ZNF671) in Multiple Tumors Based on Cancer Single-Cell Sequencing.

[This corrects the article DOI: 10.3389/fonc.2019.01214.].

The Tumor Suppressor Role of Zinc Finger Protein 671 (ZNF671) in Multiple Tumors Based on Cancer Single-Cell Sequencing.

In humans, zinc finger protein 671 (ZNF671) is a type of transcription factor. However, the contribution of tumor heterogeneity to the functional role of ZNF671 remains unknown. The present study aimed to determine the functional states of ZNF671 in cancer single cells based on single-cell sequencing datasets (scRNA-seq). We collected cancer-related ZNF671 scRNA-seq datasets and analyzed ZNF671 in the datasets. We evaluated 14 functional states of ZNF671 in cancers and performed ZNF671 expression and function state correlation analysis. We further applied t-distributed stochastic neighbor embedding to describe the distribution of cancer cells and to explore the functional state of ZNF671 in cancer subgroups. We found that ZNF671 was downregulated in eight cancer-related ZNF671 scRNA-seq datasets. Functional analysis identified that ZNF671 might play a tumor suppressor role in cancer. The heterogeneous functional states of cell subgroups and correlation analysis showed that ZNF671 played tumor suppressor roles in heterogeneous cancer cell populations. Western blot and transwell assays identified that ZNF671 inhibited EMT, migration, and invasion of CNS cancers, lung cancer, melanoma, and breast carcinoma in vitro. These results from cancer single-cell sequencing indicated that ZNF671 played a tumor suppressor role in multiple tumors and may provide us with new insights into the role of ZNF671 for cancer treatment.