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Current Cardiology Reports Oct 2020Cardiac masses frequently present significant diagnostic and therapeutic clinical challenges and encompass a broad set of lesions that can be either neoplastic or... (Review)
Review
PURPOSE OF REVIEW
Cardiac masses frequently present significant diagnostic and therapeutic clinical challenges and encompass a broad set of lesions that can be either neoplastic or non-neoplastic. We sought to provide an overview of cardiac tumors using a cardiac chamber prevalence approach and providing epidemiology, imaging, histopathology, diagnostic workup, treatment, and prognoses of cardiac tumors.
RECENT FINDINGS
Cardiac tumors are rare but remain an important component of cardio-oncology practice. Over the past decade, the advances in imaging techniques have enabled a noninvasive diagnosis in many cases. Indeed, imaging modalities such as cardiac magnetic resonance, computed tomography, and positron emission tomography are important tools for diagnosing and characterizing the lesions. Although an epidemiological and multimodality imaging approach is useful, the definite diagnosis requires histologic examination in challenging scenarios, and histopathological characterization remains the diagnostic gold standard. A comprehensive clinical and multimodality imaging evaluation of cardiac tumors is fundamental to obtain a proper differential diagnosis, but histopathology is necessary to reach the final diagnosis and subsequent clinical management.
Topics: Heart Neoplasms; Humans; Magnetic Resonance Imaging; Multimodal Imaging; Positron-Emission Tomography; Prognosis
PubMed: 33040219
DOI: 10.1007/s11886-020-01420-z -
Frontiers in Immunology 2022Head and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and...
BACKGROUND
Head and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies' development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients.
METHODS
In this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the "limma" R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website.
RESULTS
With the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy.
CONCLUSIONS
17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.
Topics: Humans; Squamous Cell Carcinoma of Head and Neck; Prognosis; Head and Neck Neoplasms; Killer Cells, Natural; Nomograms
PubMed: 36263048
DOI: 10.3389/fimmu.2022.1018685 -
Biomolecules Feb 2023(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads...
(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.
Topics: Humans; Female; RNA, Long Noncoding; Prognosis; Ferroptosis; Ovarian Neoplasms
PubMed: 36830675
DOI: 10.3390/biom13020306 -
Computers in Biology and Medicine Sep 2023Gastric carcinoma (GC) is the fourth leading cause of cancer-related mortality worldwide. Patients with advanced GC tend to have poor prognoses and shortened survival....
Identifying mitophagy-related genes as prognostic biomarkers and therapeutic targets of gastric carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.
Gastric carcinoma (GC) is the fourth leading cause of cancer-related mortality worldwide. Patients with advanced GC tend to have poor prognoses and shortened survival. Finding novel predictive biomarkers for GC prognosis is an urgent need. Mitophagy is the selection degradation of damaged mitochondria to maintain cellular homeostasis, which has been shown to play both pro- and anti-tumor effects. This study combined single-cell sequencing data and transcriptomics to screen mitophagy-related genes (MRGs) associated with GC progression and analyze their clinical values. Reverse transcription-quantitative PCR (RT-qPCR) and immunochemistry (IHC) further verified gene expression profiles. A total of 18 DE-MRGs were identified after taking an intersection of single-cell sequencing data and MRGs. Cells with a higher MRG score were mainly distributed in the epithelial cell cluster. Cell-to-cell communications among epithelial cells with other cell types were significantly upregulated. We established and validated a reliable nomogram model based on DE-MRGs (GABARAPL2 and CDC37) and traditional clinicopathological parameters. GABARAPL2 and CDC37 displayed different immune infiltration states. Given the significant correlation between hub genes and immune checkpoints, targeting MRGs in GC may supplement more benefits to patients who received immunotherapy. In conclusion, GABARAPL2 and CDC37 may be prognostic biomarkers and candidate therapeutic targets of GC.
Topics: Humans; RNA; Mitophagy; Prognosis; Stomach Neoplasms; Sequence Analysis, RNA; Carcinoma
PubMed: 37413850
DOI: 10.1016/j.compbiomed.2023.107227 -
EBioMedicine Sep 2022T cells form the major component of anti-tumor immunity. A deeper understanding of T cell exhaustion (TEX) heterogeneity within the tumor microenvironment (TME) is key...
Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy.
BACKGROUND
T cells form the major component of anti-tumor immunity. A deeper understanding of T cell exhaustion (TEX) heterogeneity within the tumor microenvironment (TME) is key to overcoming TEX and improving checkpoint blockade immunotherapies in the clinical setting.
METHODS
We conducted a comprehensive pan-cancer analysis of TEX subsets from 9564 tumor samples across 30 bulk solid cancer types. Pan-cancer TEX subtypes were identified using literature-derived hierarchical TEX-specific developmental pathway signatures. The potential multi-omics and clinical features involved in TEX heterogeneity were determined.
FINDINGS
Our study yielded a dynamic, progressive roadmap and a hierarchical dysfunction landscape regarding TEX within the TME. In total, we identified five pan-cancer TEX subtypes, revealing tissue/cancer type-specific TEX patterns in low immunogenic tumors. By contrast, highly immunogenic tumors tend to harbor high frequencies of progenitor TEX subsets. In addition, the TEX profile also revealed distinct prognoses, intrinsic molecular subtype distribution, immune microenvironment and multi-omics features among the cancers. Network analysis identified four previously unknown TEX-associated cancer genes (tolloid-like 1, myosin heavy chain 111, P2Y receptor family member 8 and protein kinase D2), the possible association with anti-PD-1 immunotherapy response was validated using a single-cell dataset. Finally, a machine learning-based gene signature was developed to model the hierarchical TEX stages, verified in single-cell and immunotherapy patient cohorts.
INTERPRETATION
Our study provided a TEX-derived system that can be applied for the immune subtyping of cancers and may have implications for the further optimization of personalized cancer immunotherapy.
FUNDING
This study was supported by the National Natural Science Foundation of China (Grant No. 62072341 and 61973240). The funders had no roles in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Topics: Humans; Immunotherapy; Myosin Heavy Chains; Neoplasms; Prognosis; Tumor Microenvironment
PubMed: 35961204
DOI: 10.1016/j.ebiom.2022.104207 -
Southern Medical Journal Dec 2021Coronavirus disease 2019 (COVID-19) is an infection caused by the severe acute respiratory syndrome-coronavirus-2 virus that led to a pandemic. Acute manifestations of... (Review)
Review
Coronavirus disease 2019 (COVID-19) is an infection caused by the severe acute respiratory syndrome-coronavirus-2 virus that led to a pandemic. Acute manifestations of COVID-19 include fever, cough, dyspnea, respiratory failure, pneumonitis, anosmia, thromboembolic events, cardiogenic shock, renal injury, ischemic strokes, encephalitis, and cutaneous eruptions, especially of hands or feet. Prolonged symptoms, unpredictable recoveries, and chronic sequelae (long COVID) sometimes emerge even for some people who survive the initial illness. Sequelae such as fatigue occasionally persist even for those with only mild to moderate cases. There is much to learn about postacute COVID-19 dyspnea, anosmia, psychosis, thyroiditis, cardiac arrhythmia, and/or multisystem inflammatory response syndrome in children. Determining prognoses is imprecise. Examining patient databases about those who have survived COVID-19 is warranted. Multidisciplinary teams are assessing such disease databases to better understand longer-term complications and guide treatment.
Topics: COVID-19; Comorbidity; Humans; Incidence; Pandemics; Prognosis; SARS-CoV-2; Post-Acute COVID-19 Syndrome
PubMed: 34853850
DOI: 10.14423/SMJ.0000000000001337 -
Frontiers in Immunology 2023Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) has significant morbidity and mortality and is associated with the induction of cytokines/chemokines,...
BACKGROUND
Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) has significant morbidity and mortality and is associated with the induction of cytokines/chemokines, which might contribute to the pathogenesis of liver injury. This study aimed to explore the cytokine/chemokine profiles of patients with HBV-ACLF and develop a composite clinical prognostic model.
METHODS
We prospectively collected blood samples and the clinical data of 107 patients with HBV-ACLF admitted to the Beijing Ditan Hospital. The concentrations of 40-plex cytokines/chemokines were measured in 86 survivors and 21 non-survivors using the Luminex assay. Discrimination between the cytokine/chemokine profiles in different prognosis groups was analyzed using the multivariate statistical techniques of principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An immune-clinical prognostic model was obtained using multivariate logistic regression analysis.
RESULTS
The PCA and PLS-DA indicated that cytokine/chemokine profiling could clearly distinguish patients with different prognoses. A total of 14 cytokines, namely, IL-1β, IL-6, IL-8, IL-10, TNF-α, IFN-γ, CXCL1, CXCL2, CXCL9, CXCL13, CX3CL1, GM-SCF, CCL21, and CCL23, were significantly correlated with disease prognosis. Multivariate analysis identified CXCL2, IL-8, total bilirubin, and age as independent risk factors that constituted the immune-clinical prognostic model, which showed the strongest predictive value of 0.938 compared with those of the Chronic Liver Failure Consortium (CLIF-C) ACLF (0.785), Model for End-Stage Liver Disease (MELD) (0.669), and MELD-Na (0.723) scores ( < 0.05 for all).
CONCLUSION
The serum cytokine/chemokine profiles correlated with the 90-day prognosis of patients with HBV-ACLF. The proposed composite immune-clinical prognostic model resulted in more accurate prognostic estimates than those of the CLIF-C ACLF, MELD, and MELD-Na scores.
Topics: Humans; Hepatitis B virus; Acute-On-Chronic Liver Failure; Cytokines; End Stage Liver Disease; Interleukin-8; Severity of Illness Index; Prognosis
PubMed: 37180134
DOI: 10.3389/fimmu.2023.1133656 -
The Journal of International Medical... Apr 2024In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating... (Review)
Review
In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.
Topics: Humans; Ischemic Stroke; Radiomics; Prognosis; Tomography, X-Ray Computed; Treatment Outcome; Stroke
PubMed: 38565321
DOI: 10.1177/03000605241238141 -
JACC. Cardiovascular Interventions May 2023
Topics: Humans; Treatment Outcome; Prognosis
PubMed: 37225295
DOI: 10.1016/j.jcin.2023.04.012 -
Clinical Cardiology Jun 2021Although anxiety is highly prevalent after myocardial infarction (MI), but the association between anxiety and MI is not well established. This study aimed to provide an... (Meta-Analysis)
Meta-Analysis Review
Although anxiety is highly prevalent after myocardial infarction (MI), but the association between anxiety and MI is not well established. This study aimed to provide an updated and comprehensive evaluation of the association between anxiety and short-term and long-term prognoses in patients with MI. Anxiety is associated with poor short-term and long-term prognoses in patients with MI. We performed a systematic search in the PubMed and Cochrane databases (January 2000-October 2020). The study endpoints were complications, all-cause mortality, cardiac mortality, and/or major adverse cardiac events (MACEs). Pooled data were synthesized using Stata SE12.0 and expressed as risk ratios (RRs) and 95% confidence intervals (CIs). We included 9373 patients with MI from 16 published studies. Pooled analyses indicated a correlation between high anxiety and poor clinical outcomes (RR: 1.19, 95% CI: 1.13-1.26, p < .001), poor short-term complications (RR: 1.23, 95% CI: 1.09-1.38, p = .001), and poor long-term prognosis (RR: 1.27, 95% CI: 1.13-1.44, p < .001). Anxiety was also specifically associated with long-term mortality (RR: 1.16, 95% CI: 1.01-1.33, p = .033) and long-term MACEs (RR: 1.54, 95% CI: 1.26-1.90, p < .001). This study provided strong evidence that increased anxiety was associated with poor prognosis in patients with MI. Further analysis revealed that MI patients with anxiety had a 23% increased risk of short-term complications and a 27% increased risk of adverse long-term prognosis compared to those without anxiety.
Topics: Anxiety; Humans; Myocardial Infarction; Prognosis
PubMed: 33960435
DOI: 10.1002/clc.23605