BackgroundHuman papillomavirus-positive (HPV+) cervical cancers are highly heterogeneous in clinical and molecular characteristics. Thus, an investigation into their heterogeneous immunological profiles is meaningful in providing both biological and clinical insights into this disease.MethodsBased on the enrichment of 29 immune signatures, we discovered immune subtypes of HPV+ cervical cancers by hierarchical clustering. To explore whether this subtyping method is reproducible, we analyzed three bulk and one single cell transcriptomic datasets. We also compared clinical and molecular characteristics between the immune subtypes.ResultsClustering analysis identified two immune subtypes of HPV+ cervical cancers: Immunity-H and Immunity-L, consistent in the four datasets. In comparisons with Immunity-L, Immunity-H displayed stronger immunity, more stromal contents, lower tumor purity, proliferation potential, intratumor heterogeneity and stemness, higher tumor mutation burden, more neoantigens, lower levels of copy number alterations, lower DNA repair activity, as well as better overall survival prognosis. Certain genes, such as MUC17, PCLO, and GOLGB1, showed significantly higher mutation rates in Immunity-L than in Immunity-H. 16 proteins were significantly upregulated in Immunity-H vs. Immunity-L, including Caspase-7, PREX1, Lck, C-Raf, PI3K-p85, Syk, 14-3-3_epsilon, STAT5-α, GATA3, Src_pY416, NDRG1_pT346, Notch1, PDK1_pS241, Bim, NF-kB-p65_pS536, and p53. Pathway analysis identified numerous immune-related pathways more highly enriched in Immunity-H vs. Immunity-L, including cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, antigen processing and presentation, T/B cell receptor signaling, chemokine signaling, supporting the stronger antitumor immunity in Immunity-H vs. Immunity-L.ConclusionHPV+ cervical cancers are divided into two subgroups based on their immune signatures' enrichment. Both subgroups have markedly different tumor immunity, progression phenotypes, genomic features, and clinical outcomes. Our data offer novel perception in the tumor biology as well as clinical implications for HPV+ cervical cancer.
Objective. Electrocardiogram (ECG) is an important diagnostic tool that has been the subject of much research in recent years. Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. Approach. A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. A new multimodel method was constructed by fusing the random forest and RESNET approaches. Main Results. Owing to its ability to combine discriminative human-crafted features with RESNET deep features, the proposed new method showed over 88% classification accuracy and yielded the best results in comparison with alternative methods. Significance. A new multimodel fusion method was presented for abnormal cardiovascular detection based on ECG data. The experimental results show that separable convolution and multiscale convolution are vital for ECG record classification and are effective for use with one-dimensional ECG sequences.
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