Mitochondrial adenosine triphosphate (ATP) synthase is the key enzyme of mitochondrial oxidative phosphorylation reaction.The down-regulation of the mitochondrial ATP synthase is a hallmark of most human carcinomas, which is the embodiment of the bioenergetic signature of cancer in the performance of the decreased oxidative phosphorylation and increased aerobic glycolysis. Combining with the bioenergetic signature of cancer, studies showed that mitochondrial ATP synthase and multidrug resistance and adverse prognosis of tumor were closely related. Its mechanisms are related to post-transcriptional regulation of the ATP synthase,the hypermethylation of the ATP synthase gene and the inhibitor peptide of the mitochondrial ATP synthase, called ATP synthase inhibitory factor 1(IF1). In this review, we stress the biological characteristics of mitochondrial ATP synthase and the relationship between ATP synthase and multidrug resistance and prognosis of Malignant tumor, in order to find a new way for tumor therapy.
Traditional classifiers, such as support vector machine and Bayesian classifier, require data normalization for removing experimental batch effects, which limit their applications at the individual level. In this paper, we aim to build a classifier to distinguish lung cancer and non-cancer lung tissues (pneumonia and normal lung tissues). We identified gene pairs as signatures to build a classifier based on the within-sample relative expression orderings of gene pairs in a particular type of tissues (cancer or non-cancer). Using multiple independent datasets as the training data, including a total of 197 lung cancer cases and 189 non-cancer cases, we identified three gene pairs. Classifying a sample by the majority voting rule, the average accuracy reached 95.34% in the training data. Using multiple independent validation datasets, including a total of 251 lung cancer samples and 141 non-cancer samples without data normalization, the average accuracy was as high as 96.78%. The rank-based signature is robust against experimental batch effects and can be used to diagnose lung cancer using samples measured by different laboratories at the individual level.
ObjectiveTo explore the immune biomarkers for prognosis of breast cancer and to construct a risk assessment model.MethodsThe gene expression of breast cancer samples was retrieved from The Cancer Genome Map (TCGA) database and immune related genes (IRGs) were retrieved from the ImmPort database. Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression were used for prognostic analysis. Gene set enrichment analysis ( GSEA) was used to explore biological signaling pathways. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment.ResultsNine kinds of immune-related differentially expressed genes independently related to prognosis were identified: adrenoceptor beta 1 (ADRB1), interleukin 12B (IL12B), syndecan 1 (SDC1), thymic stromal lymphopoietin (TSLP), fibroblast growth factor 19 (FGF19), fatty acid binding protein 7 (FABP7), interferon epsilon (IFNE), tumor necrosis factor receptor superfamily member 18 (TNFRSF18) and interleukin 27 (IL27). The risk assessment equation constructed by these nine kinds of genes had powerful predictive ability. The “neurotrophin signaling pathway” and “adipocyte factor signaling pathway” were activated in patients of high-risk group, and “leukocyte transendothelial migration” “WNT signaling pathway” “FcεRI signaling pathway” “valine, leucine and isoleucine biosynthesis” and “protein export pathway” were activated in patients of low-risk group. A variety of tumor-killing immune cells were significantly enriched in the tumor-infiltrating immune cells of patients in the low-risk group. The immunosuppressive immune cells were significantly enriched in tumor infiltrating immune cells of patients in high-risk group.ConclusionIRGs prognostic signatures are an effective potential predictive classifier in breast cancer treatment.