Bioinformatic Analysis on the Prognostic Value of Neurotransmitter Receptor-Related Genes in Kidney Renal Clear Cell Carcinoma.

Context: Kidney renal clear-cell carcinoma (KIRC) is a malignant tumor. At an early stage, KIRC patients may experience only mild fever and fatigue or even no symptoms, and these early nonspecific indications can delay treatment. Neurotransmitters and their receptors may be very useful in determining tumorigenesis and predicting metastasis. Objective: The study intended to investigate the predictive value of neurotransmitter receptor-related genes (NRRGs) using public KIRC data, by determining the biological processes that implicate the prognostic NRRGs and establishing a predictive NR-related risk model, to provide an empirical basis for identifying and treating KIRC patients. Design: The research team performed a genetic case-control study. Setting: The study took place at Research Center of Health, Big Data Mining and Applications, Wannan Medical College, Wuhu, China. Methods: The research team: (1) obtained the transcriptome data related to KIRC from the Cancer Genome Atlas (TCGA) and ArrayExpress databases; (2) developed the differentially expressed NRRGs (DENRRGs) by identifying the NRRGs that intersected with DEGs in KIRC and normal samples; (3) carried out functional enrichment analyses of the DENRRGs; (4) screened the characteristic genes of the DENRRGs using machine learning; (5) created a predictive model using multivariate Cox analyses of the distinctive genes; (6) obtained independent prognostic factors for KIRC patients and established a nomograph model; (7) investigated the sensitivity of KIRC patients to therapeutic agents to examine the variations in immunological features between high-risk and low-risk individuals. Results: Differential analysis found that 115 NRRGs intersected with 5275 DEGs to provide 52 DENRRGs. Functional enrichment showed that DENRRGs were mainly involved in signal transduction in the nervous system. The machine learning on the 52 DENRRGs filtered out nine characteristic genes. Subsequently, the research team found eight prognostic biomarkers-histamine receptor H2 (HRH2), gamma-aminobutyric acid (GABA) receptor subunit epsilon (GABRE), cholinergic receptor nicotinic delta subunit (CHRND), glutamate receptor ionotropic subunit 2D (GRIN2D), glutamate metabotropic receptor 4 (GRM4), glycine receptor alpha 3 (GLRA3), cholinergic receptor nicotinic beta 4 subunit (CHRNB4), and cholinergic receptor muscarinic-1 (CHRM1)-and established a predictive model. Furthermore, the team precisely predicted the KIRC patients' prognoses using a nomogram that combined their ages, risk scores, and M stages. The infiltration levels of 21 immune cells also significantly differed between the high-risk and low-risk groups, with neutrophils having a significant positive correlation with GABRE and HRH2 and a significant negative correlation with CHRNB4 and GRM4. Finally, the 50% inhibitory concentration (IC50) values for various drugs, such as 5-aminoimidazole-4-carboxamide-1-ß-D-ribofuranoside (AICAR), 8-hydroxy-7-(6-sulfonaphthalen-2-yl)diazenyl-quinoline-5-sulfonic acid (NSC-87877), Sunitinib, c-Jun N-terminal kinase (JNK) inhibitor VIII, and tanespimyci (X17.AAG) were significantly lower for high-risk group. Conclusions: By studying the relevance of biomarkers to the immunological microenvironment of KIRC, the current research team was able to propose a new predictive model for KIRC based on NRRGs, to offer a novel viewpoint for investigating KIRC. The study's results suggest new avenues for research into the pathophysiology and therapy of KIRC. Determining the precise molecular processes by which predictive biomarkers regulate KIRC requires further evidence and analysis.

Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss.

In this study, deep learning and triplet loss function methods are used for finger vein verification research, and the model is trained and validated between different kinds of datasets including FV-USM, HKPU, and SDUMLA-HMT datasets. This work gives the accuracy and other evaluation indexes of finger vein verification calculated for different training-validation set combinations and gives the corresponding ROC curves and AUC values. The accuracy of the best result has reached 98%, and all the ROC AUC values are above 0.98, indicating that the obtained model can identify the finger veins well. Since the experiments are cross-validated between different kinds of datasets, the model has good adaptability and applicability. From the experimental results, it is also found that the model trained on the dataset that is more difficult to be distinguished will be a better and more robust model.