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Background: T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA). Methods: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature. We analyzed the association of different risk groups with survival analysis, gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and immunotherapy response. Results: Based on 192 T-cell marker genes identified by scRNA-seq analysis, we constructed a prognostic signature containing 7 genes in the training cohort, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5 years were 0.734, 0.742 and 0.726 in the training cohort, 0.697, 0.671 and 0.670 in the testing cohort, 0.702, 0.665 and 0.629 in the GEO cohort, respectively. In addition, we constructed a nomogram based on clinical factors and the risk score of the signature. The low-risk group exhibited higher immune-related pathways, immune cell infiltration and TMB levels. Importantly, immunophenotype score and immunotherapy cohort (IMvigor210) analyses showed that the low-risk group had better immunotherapy response and prognosis. Conclusions: Our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for BLCA patients. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual Learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of non-uniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH-2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1-score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1-score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the efficacy of deep learning architectures in enhancing the interpretation of skin images, thereby aiding in the classification and segmentation of skin cancer. However, the existing deep learning techniques predominantly focus on either segmentation or classification and are designed for normally distributed data. Addressing this limitation, the present study introduces a hybrid ensemble deep learning approach for skin cancer classification and segmentation. This proposed model amalgamates Residual Learning Machines, Swin Transformers, and Fast Neural Networks (FNN) to proficiently manage diverse sets of non-uniformly distributed data, thereby augmenting diagnostic accuracy. The efficacy of this approach was evaluated using various skin cancer datasets, including ISIC-2008, PH-2, and HM007. The assessment involved several performance metrics, such as the Matthew correlation coefficient (MCC), recall, F1-score, specificity, accuracy, and precision. Moreover, to underscore the superiority of the proposed model, its performance was juxtaposed with that of previous efforts. Results from repeated trials indicate that the proposed model achieved 98.78% classification accuracy, 98.7% precision, 98.7% F1-score, 98.64% average recall, and an MCC of 0.9863 across different skin disease datasets. Demonstrating consistent superiority over existing methods, the proposed approach shows considerable potential in revolutionizing the diagnostics of skin cancer. [ABSTRACT FROM AUTHOR]

Exosomes-related long non-coding RNAs (lncRNAs) have been reported to play significant roles in clear cell renal cell carcinoma (ccRCC). However, there is little known about the relationship between exosomes-related lncRNAs and ccRCC. This study aimed to select optimal prognostic model based on exosomes-related lncRNAs to provide a methodological reference for high-dimensional data. Based on the Cancer Genome Atlas (TCGA) database of 515 ccRCC patients, two risk score models were generated underlying Bayesian spike-and-slab lasso and lasso regression. The optimal model was determined by calculating the area of time-dependent receiver-operating characteristic (ROC) curves in the TCGA and ArrayExpress databases. The immune patterns and sensitivity of immunotherapy between the high and low groups were further explored. Initially, we constructed two risk score models containing 11 and 7 exosomes-related lncRNAs according to Bayesian spike-and-slab lasso and lasso regression respectively. ROC curves revealed that the model constructed by Bayesian spike-and-slab lasso regression was more reliable in predicting survival at 1, 3, and 5 years, yielding an area under the curves (AUCs) of 0.796, 0.732, and 0.742, respectively. Kaplan–Meier (K-M) curves presented that prognosis was poorer in the high-risk score group (P < 0.001). Additionally, the high-risk score group patients were enriched in immune-activating phenotypes and more sensitive to immunotherapy. The exosomes-related lncRNAs model constructed with Bayesian spike-and-slab lasso regression has higher predictive power for ccRCC patients’ prognosis, which provides methodological reference for the analysis of high-dimensional data in bioinformatics and guides the tailored treatment of ccRCC patients. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests. Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes. We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission. The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data. We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice. • The MTaSPUsSet, a novel adaptive association test, considers association patterns. • More pleiotropic genes were found by aggregating multiple related phenotypes. • It is a cost-effective study based on GWAS summary statistics. [ABSTRACT FROM AUTHOR]