Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients.

The incidence of breast cancer (BC) continues to rise steadily, posing a significant burden on the public health systems of various countries worldwide. As a member of the tumor microenvironment (TME), CD8+ T cells inhibit cancer progression through their protective role. This study aims to investigate the role of CD8+ T cell-related genes (CTRGs) in breast cancer patients. METHODS: We assessed the abundance of CD8+ T cells in the TCGA and METABRIC datasets and obtained CTRGs through WGCNA. Subsequently, a prognostic signature (CTR score) was constructed from CTRGs screened by seven machine learning algorithms, and the relationship between the CTR score and TME, immunotherapy, and drug sensitivity was analyzed. Additionally, CTRGs' expression in different cells within TME was identified through single-cell analysis and spatial transcriptomics. Finally, the expression of CTRGs in clinical tissues was verified via RT-PCR. RESULTS: The CD8+ T cell-related prognostic signature consists of two CTRGs. In the TCGA and METABRIC datasets, the CTR score appeared to be negatively linked to the abundance of CD8+ T cells, and BC patients with higher risk score show a worse prognosis. The low CTR score group exhibits higher immune infiltration levels, closely associated with inhibiting the tumor microenvironment. Compared with the high CTR score group, the low CTR score group shows better responses to chemotherapy and immune checkpoint therapy. Single-cell analysis and spatial transcriptomics reveal the heterogeneity of two CTRGs in different cells. Compared with the adjacent tissues, CD163L1 and KLRB1 mRNA are downregulated in tumor tissues. CONCLUSIONS: This study establishes a robust CD8+ T cell-related prognostic signature, providing new insights for predicting the clinical outcomes and treatment responses of breast cancer patients.

Effects of diverse resistance training modalities on performance measures in athletes: a network meta-analysis.

Background: Jumping ability is one of the necessary qualities for athletes. Previous studies have shown that plyometric training and complex training including plyometrics can improve athletes' jumping ability. With the emergence of various types of complex training, there is uncertainty about which training method has the best effect. This study conducted a meta-analysis of randomized controlled trials of plyometric-related training on athletes' jumping ability, to provide some reference for coaches to design training plans. Methods: We systematically searched 3 databases (PubMed, Web of Science, and Scopus) up to July 2023 to identify randomized controlled trials investigating plyometrics related training in athletes. The two researchers conducted literature screening, extraction and quality assessment independently. We performed a network meta-analysis using Stata 16. Results: We analyzed 83 studies and found that complex training, which includes high-intensity intervals and plyometric exercises, was the most effective method for improving squat jumps (SURCA = 96%). In the case of countermovement jumps a combination of electrostimulation and plyometric training yielded the best results (SURCA = 97.6%). Weightlifting training proved to be the most effective for the standing long jump (SURCA = 81.4%), while strength training was found to be the most effective for the five bounces test (SURCA = 87.3%). Conclusion: Our current study shows that complex training performs more efficient overall in plyometric-related training. However, there are different individual differences in the effects of different training on different indicators (e.g., CMJ, SJ, SLJ, 5BT) of athletes. Therefore, in order to ensure that the most appropriate training is selected, it is crucial to accurately assess the physical condition of each athlete before implementation. Clinical Trial Registration: https://www.crd.york.ac.uk/PROSPERO/, Registration and protocol CRD42023456402.