Esophageal carcinoma (ESCA) refers to the most common type of malignant tumor, which reveals that it occurs often all over the world. ESCA is also correlated with an advanced stage and low survival rates. Thus, the development of new prognostic biomarkers is an absolute necessity. In this study, the aim was to investigate the potential of COX7B as a brand-new predictive biomarker for ESCA patients. COX7B expression in pancancer was examined using TIMER2. The statistical significance of the predictive value of COX7B expression was explored. The relationship between COX7B expression and tumor-infiltrating immune cells in ESCA was analyzed by using ssGSEA. In this study, the result indicated that several types of cancers had an abnormally high amount of COX7B. COX7B expression in samples from patients with ESCA was considerably higher than in nontumor tissues. A more advanced clinical stage may be anticipated from higher COX7B expression. According to the findings of Kaplan-Meier survival curves, patients with low COX7B levels had a more favorable prognosis than those with high COX7B levels. The result of multivariate analysis suggested that COX7B expression was a standalone prognostic factor for the overall survival of ESCA patients. A prognostic nomogram including gender, clinical stage, and COX7B expression was constructed, and TCGA-based calibration plots indicated its excellent predictive performance. An analysis of immune infiltration revealed that COX7B expression has a negative correlation with TFH, Tcm, NK cells, and mast cells. COX7B may serve as an immunotherapy target and as a biomarker for ESCA diagnosis and prognosis.

Gastric cancer (GC) is a highly molecular heterogeneous tumor with unfavorable outcomes. The Notch signaling pathway is an important regulator of immune cell differentiation and has been associated with autoimmune disorders, the development of tumors, and immunomodulation caused by tumors. In this study, by developing a gene signature based on genes relevant to the Notch pathway, we could improve our ability to predict the outcome of patients with GC. From the TCGA database, RNA sequencing data of GC tumors and associated normal tissues were obtained. Microarray data were collected from GEO datasets. The Molecular Signature Database (MSigDB) was accessed in order to retrieve sets of human Notch pathway-related genes (NPRGs). The LASSO analysis performed on the TCGA cohort was used to generate a multigene signature based on prognostic NPRGs. In order to validate the gene signature, the GEO cohort was utilized. Using the CIBERSORT method, we were able to determine the amounts of immune cell infiltration in the GC. In this study, a total of 21 differentially expressed NPRGs were obtained between GC specimens and nontumor specimens. The construction of a prognostic prediction model for patients with GC involved the identification and selection of three different NPRGs. According to the appropriate cutoff value, the patients with GC were divided into two groups: those with a low risk and those with a high risk. The time-dependent ROC curves demonstrated that the new model had satisfactory performance when it came to prognostic prediction. Multivariate assays confirmed that the risk score was an independent marker that may be used to predict the outcome of GC. In addition, the generated nomogram demonstrated a high level of predictive usefulness. Moreover, the scores of immunological infiltration of the majority of immune cells were distinctly different between the two groups, and the low-risk group responded to immunotherapy in a significantly greater degree. According to the results of a functional enrichment study of candidate genes, there are multiple pathways and processes associated with cancer. Taken together, a new gene model associated with the Notch pathway may be utilized for the purpose of predicting the prognosis of GC. One potential method of treatment for GC is to focus on NPRGs.

In order to optimize the computer management of smart medical laboratory services and find the optimal solution, we conducted experiments on the laboratory computers of hospitals in this city based on the RBF neural network, which provided references for other researchers. Through the collection of relevant data, this article summarizes and analyzes the existing medical laboratory research, summarizes the existing problems and development directions of the current laboratory, uses the RBF neural network to modify these models, and innovatively achieves a hospital laboratory computer management optimization system with the characteristics of high efficiency, low energy consumption, and fast response. The experimental results prove that the computer management and optimization of laboratory services are optimized through the RBF neural network, and the efficiency of computer management design and optimization is greatly improved. It is about 20% higher than traditional medical laboratory. This shows that the computer management design and optimization of smart medical laboratory services designed by RBF neural network can play an important role in the construction of hospital laboratories.