Blood-based test has been considered as a promising way to diagnose and study Alzheimer's disease (AD). However, the changed proportions of the leukocytes under disease states could confound the aberrant expression signals observed in mixed-cell blood samples. We have previously proposed a method, Ref-REO, to detect the leukocyte specific expression alterations from mixed-cell blood samples. In this study, by applying Ref-REO, we detect 42 and 45 differentially expressed genes (DEGs) between AD and normal peripheral whole blood (PWB) samples in two datasets, respectively. These DEGs are mainly associated with AD-associated functions such as Wnt signaling pathways and mitochondrion dysfunctions. They are also reproducible in AD brain tissue, and tend to interact with the reported AD-associated biomarkers and overlap with targets of AD-associated PWB miRNAs. Moreover, they are closely associated with aging and have severer expression alterations in the younger adults with AD. Finally, diagnostic signatures are constructed from these leukocyte specific alterations, whose area under the curve (AUC) for predicting AD is higher than 0.73 in the two AD PWB datasets. In conclusion, gene expression alterations in leukocytes could be extracted from AD PWB samples, which are closely associated with AD progression, and used as a diagnostic signature of AD.
Owing to the remarkable heterogeneity of gastric cancer (GC), population-level differentially expressed genes (DEGs) identified using case-control comparison cannot indicate the dysregulated frequency of each DEG in GC. In this work, first, the individual-level DEGs were identified for 1,090 GC tissues without paired normal tissues using the RankComp method. Second, we directly compared the gene expression in a cancer tissue to that in paired normal tissue to identify individual-level DEGs among 448 paired cancer-normal gastric tissues. We found 25 DEGs to be dysregulated in more than 90% of 1,090 GC tissues and also in more than 90% of 448 GC tissues with paired normal tissues. The 25 genes were defined as universal DEGs for GC. Then, we measured 24 paired cancer-normal gastric tissues by RNA-seq to validate them further. Among the universal DEGs, 4 upregulated genes (BGN, E2F3, PLAU, and SPP1) and 1 downregulated gene (UBL3) were found to be cancer genes already documented in the COSMIC or F-Census databases. By analyzing protein-protein interaction networks, we found 12 universally upregulated genes, and we found that their 284 direct neighbor genes were significantly enriched with cancer genes and key biological pathways related to cancer, such as the MAPK signaling pathway, cell cycle, and focal adhesion. The 13 universally downregulated genes and 16 direct neighbor genes were also significantly enriched with cancer genes and pathways related to gastric acid secretion. These universal DEGs may be of special importance to GC diagnosis and treatment targets, and they may make it easier to study the molecular mechanisms underlying GC.
Background
The qualitative transcriptional characteristics, the within‐sample relative expression orderings (REOs) of genes, are highly robust against batch effects and sample quality variations. Hence, we develop a qualitative transcriptional signature based on REOs to predict the biochemical recurrence risk of prostate cancer (PCa) patients after radical prostatectomy.
Methods
Gene pairs with REOs significantly correlated with the biochemical recurrence‐free survival (BFS) were identified from 131 PCa samples in the training data set. From these gene pairs, we selected a qualitative transcriptional signature based on the within‐sample REOs of gene pairs which could predict the recurrence risk of PCa patients after radical prostatectomy.
Results
A signature consisting of 74 gene pairs, named 74‐GPS, was developed for predicting the recurrence risk of PCa patients after radical prostatectomy based on the majority voting rule that a sample was assigned as high risk when at least 37 gene pairs of the 74‐GPS voted for high risk; otherwise, low risk. The signature was validated in six independent datasets produced by different platforms. In each of the validation datasets, the Kaplan‐Meier survival analysis showed that the average BFS of the low‐risk group was significantly better than that of the high‐risk group. Analyses of multiomics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups.
Conclusions
The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multiomics characteristics. Hence, 74‐GPS may serve as a helpful tool for guiding the management of PCa patients with radical prostatectomy at the individual level.
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