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An oncoplot plot showed that missense mutations occupied an absolute position among total mutations and that the number of mutations in low-risk samples was higher than that in high-risk samples Figure 5A. Then, a transition and transversion plot was used to classify single-nucleotide variants into six categories i. Mutation information of high-risk samples and low-risk samples.
A,B Statistic of variant classification and mutation type of samples with high-risk top panel and low-risk bottom panel survival. C Driver mutation genes in high-risk and low-risk samples top and bottom panel respectively. D Mutational signatures were identified in high-risk and low-risk samples, respectively.
Cancer driver genes could provide an advantage for the selective growth of cancer cells. Whereas the Oncodrive algorithm was more advantageous in terms of sensitivity in identifying oncogenes with mutational hotspots, in contrast, the Oncodrive algorithm underperformed in identifying potential tumor suppressor genes Mayakonda et al.
In consequence, we did not identify potential tumor suppressor genes, such as TP As cancer progresses, a characteristic mutational pattern may be left behind at various points in time, which may reveal its underlying mutagenic process Alexandrov et al. In total, we identified six and eight signatures in high-risk and low-risk samples, respectively Figure 5D. This may be due to the generation of abasic sites after removal of uracil by base excision repair.
The mutational process underlying this signature was altered activity of the error-prone polymerase POLE. It has been proposed that the presence of large numbers of this signature was associated with recurrent POLE somatic mutations.
The observations from signature enrichment suggest that DNA mismatch repair mechanisms play a crucial role in the development of malignant tumors, in accordance with previous reports Belfield et al.
To further clarify the intrinsic biological differences between high-risk and low-risk samples, the ESTIMATE algorithm was used for the estimation of stromal cells and immune cells in malignant tumors by calculating the corresponding scores. A higher immune score or stromal score represents a larger amount of immune or stromal components in the tumor microenvironment.
R and P were calculated by the Pearson test. We further compared the enrichment levels of different types of cells from gene expression data for 64 types of immune and stromal cells in the two types of samples Figure 7A , Supplementary Table S3. Additionally, we found infiltration scores of stromal cells, such as endothelial cells, fibroblasts, and pericytes, were relatively high in high-risk samples compared to low-risk samples.
Subsequently, on the basis of data for the high-risk and low-risk samples, we generated a heatmap of immune cells with significant differences and performed a differential analysis of gene expression of the immune checkpoint PD-L1 Figures 7B—D. Using the HR-LR model, the low-risk samples were associated with a favorable prognosis with a median survival time of 2, days, while the high-risk samples had a median survival time of days.
PD-L1 has been found to be an immune checkpoint. We selected CpG islands and open-sea regions to measure methylation levels in each STAD sample by the method described in the Materials and Methods section. The relationships between global methylation, promoter methylation, and expression of PD-L1 and DNA repair processes need to be further studied. Moreover, we also calculated the expression of other immune checkpoints. We found that high-risk samples were characterized by a significantly higher expression level of immune checkpoint genes including CD27, CD40, and CD Several studies have shown that tumors with a higher TMB can produce more neoantigens, which are more easily recognized by T cells and induce greater immune cytotoxic activity Rooney et al.
A The infiltration of immune and stromal cell types as well as the immune-related scores in high-risk and low-risk samples.
B The heatmap of infiltration degree of those immune and stromal cell types with significantly different infiltration in high- and low-risk samples. Cell types marked with orange color represent higher infiltration in high-risk samples while cell types marked with green color represent higher infiltration in low-risk samples. C Genome-wide hypermethylation in CGI regions and hypomethylation in open sea regions.
D Promoter methylation and expression of PD-L1 in high-risk and low-risk samples. As described in the first Results subsection, we found that SSA and NHEJ have low activity in normal cells because less replication occurs in normal cells in comparison with cancer cells. This hypothesis was validated, as shown in Figure 8A. From the receiver operating characteristic plot, we selected 0. Five measures were utilized to evaluate the performance of the Cancer—Normal model, namely, the true positive rate or sensitivity , 1—the false positive rate or specificity , accuracy, precision, and the F-measure.
All these measures showed that the Cancer—Normal model gives good predictions of the status of individuals Figure 8D. Constructing Cancer-Normal model and predicting the status of samples. AUC values are listed. We then evaluated these two models by comparing their predictive accuracy with that of random models. By randomly selecting 76 genes from the expression profiles, computing random scores, and selecting a cut-off as in the case of the real models, we constructed a random HR-LR model and Cancer—Normal model.
Together, these results validated the effectiveness of our HR-LR model and Cancer—Normal model and confirmed that their predictive accuracy was not randomly achieved. Evaluation of two models and combing clinical features to predict risk assessment for individuals. D Decision tree to predict the patient’s clinical outcome. The result of samples in the TCGA cohort is listed at to bottom.
E A nomogram plot is constructed to quantify risk assessment for an individual patient. On the basis of this result, we constructed a decision tree to help predict the clinical outcome for an individual Figure 9D. In addition, a nomogram was constructed to quantify the survival probability for individual GC patients Figure 9E.
The SSA-NHEJ-derived class high-risk or low-risk and several clinicopathological features were included in the nomogram, such as age, gender, disease stage, and disease grade. The C-index reached 0. The calibration curve also indicated good agreement between the estimates and observations, which suggested that our nomogram had a high level of accuracy Supplementary Figure S2. The decision tree and nomogram could contribute to the prognosis in the case of an individual.
DNA repair is a vital biological process in normal physiological conditions and includes various types of repair approaches, such as base excision repair, mismatch repair, and DSB repair. Some repair methods could reduce the number of mutations in individuals, while other repair approaches such as SSA and NHEJ might result in fairly large errors and lead to the accumulation of many somatic mutations Negrini et al. These 76 genes comprised 37 positively correlated genes and 39 negatively correlated genes, which were further subjected to the t -test to obtain the value of the t -statistic for each sample.
In our study, we further confirmed this conclusion. On the other hand, we found that SSA and NHEJ activities were significantly higher in cancer samples with good outcomes in comparison with normal samples, but the increase was significantly less in cancer samples with poor outcomes.
On the basis of this phenomenon, we suggest that in normal physiological conditions biological activities such as DNA replication are maintained within normal limits, and hence the SSA and NHEJ approaches are not much needed.
There have also been many studies that focused on the relationship between TMB and immune infiltration. Some studies found that a higher TMB is associated with more immune infiltration, while some studies obtained the opposite result. In this study, low-risk samples were found to have a higher TMB but less infiltration of general immune cells. In addition, we found that PD-L1 had higher expression levels in low-risk samples.
Similarly, other investigators also found that CD8 positivity is significantly associated with PD-L1 expression Vidotto et al. The relationship between the TMB and immune infiltration and their roles in GC needs to be further investigated. The HR-LR model was constructed on the basis of the training set of sequencing data from the Illumina platform. The predictive accuracy of the model was validated in the test set, which also comprised sequencing data.
Furthermore, the prognostic value of the HR-LR model was validated in several GEO datasets, which contained microarray data from different platforms. Similarly, the Cancer—Normal model was also constructed on the basis of Illumina sequencing data and validated in microarray data from different platforms.
In addition, we validated the accuracy of the two models by comparing them with random models. Epstein-Barr virus: more than 50 years old and still providing surprises. Nat Rev Cancer. J Virol. The Epstein-Barr virus lytic program is controlled by the co-operative functions of two transactivators. EMBO J. Epstein-Barr virus late gene transcription depends on the assembly of a virus-specific preinitiation complex. Human immunity against EBV-lessons from the clinic.
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Oxidative stress induces reactivation of Kaposi’s sarcoma-associated herpesvirus and death of primary effusion lymphoma cells. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.
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Constructing Cancer-Normal model and predicting the status of samples. XLSX Click here for additional data file. The new PMC design is here! The Epstein-Barr virus lytic program is controlled by the co-operative functions of two transactivators.