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Background: With the improved knowledge of disease biology and the introduction of immune checkpoints, there has been significant progress in treating renal cell carcinoma (RCC) patients. Individual treatment will differ according to risk stratification. As the clinical course varies in RCC, it has developed different predictive models for assessing patient's individual risk. However, among other prognostic scores, no transparent preference model was given. MicroRNA as a putative marker shown to have prognostic relevance in RCC, molecular analysis may provide an innovative benefit in the prophetic prediction and individual risk assessment. Therefore, this study aimed to establish a prognostic-related microRNA risk score model of RCC and further explore the relationship between the model and the immune microenvironment, immune infiltration, and immune checkpoints. This practical model has the potential to guide individualized surveillance protocols, patient counseling, and individualized treatment decision for RCC patients and facilitate to find more immunotherapy targets.Methods: Downloaded data of RCC from the TCGA database for difference analysis and divided it into a training set and validation set. Then the prognostic genes were screened out by Cox and Lasso regression analysis. Multivariate Cox regression analysis was used to establish a predictive model that divided patients into high-risk and low-risk groups. The ENCORI online website and the results of the RCC difference analysis were used to search for hub genes of miRNA. Estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment (TME) and immune infiltration. Based on Kaplan-Meier survival analysis, search for immune checkpoints related to the prognosis of RCC.Results: There were nine miRNAs in the established model, with a concordance index of 0.702 and an area under the ROC curve of 0.701. Nine miRNAs were strongly correlated with the prognosis (P < 0.01), and those with high expression levels had a poor prognosis. We found a common target gene PDGFRA of hsa-miR-6718, hsa-miR-1269b and hsa-miR-374c, and five genes related to ICGs (KIR2DL3, TNFRSF4, LAG3, CD70 and TNFRSF9). The immune/stromal score, immune infiltration, and immune checkpoint genes of RCC were closely related to its prognosis and were positively associated with a risk score.Conclusions: The established nine-miRNAs prognostic model has the potential to facilitate prognostic prediction. Moreover, this model was closely related to the immune microenvironment, immune infiltration, and immune checkpoint genes of RCC. [ABSTRACT FROM AUTHOR]

[Display omitted] Cancer is characterized by metabolic reprogramming, which is a hot topic in tumor treatment research. Cancer cells alter metabolic pathways to promote their growth, and the common purpose of these altered metabolic pathways is to adapt the metabolic state to the uncontrolled proliferation of cancer cells. Most cancer cells in a state of nonhypoxia will increase the uptake of glucose and produce lactate, called the Warburg effect. Increased glucose consumption is used as a carbon source to support cell proliferation, including nucleotide, lipid and protein synthesis. In the Warburg effect, pyruvate dehydrogenase activity decreases, thereby disrupting the TCA cycle. In addition to glucose, glutamine is also an important nutrient for the growth and proliferation of cancer cells, an important carbon bank and nitrogen bank for the growth and proliferation of cancer cells, providing ribose, nonessential amino acids, citrate, and glycerin necessary for cancer cell growth and proliferation and compensating for the reduction in oxidative phosphorylation pathways in cancer cells caused by the Warburg effect. In human plasma, glutamine is the most abundant amino acid. Normal cells produce glutamine via glutamine synthase (GLS), but the glutamine synthesized by tumor cells is insufficient to meet their high growth needs, resulting in a "glutamine-dependent phenomenon." Most cancers have an increased glutamine demand, including breast cancer. Metabolic reprogramming not only enables tumor cells to maintain the reduction–oxidation (redox) balance and commit resources to biosynthesis but also establishes heterogeneous metabolic phenotypes of tumor cells that are distinct from those of nontumor cells. Thus, targeting the metabolic differences between tumor and nontumor cells may be a promising and novel anticancer strategy. Glutamine metabolic compartments have emerged as promising candidates, especially in TNBC and drug-resistant breast cancer. In this review, the latest discoveries of breast cancer and glutamine metabolism are discussed, novel treatment methods based on amino acid transporters and glutaminase are discussed, and the relationship between glutamine metabolism and breast cancer metastasis, drug resistance, tumor immunity and ferroptosis are explained, which provides new ideas for the clinical treatment of breast cancer. [ABSTRACT FROM AUTHOR]