Imaging Source Site ISS Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource.
We would like to acknowledge the individuals and institutions that have provided data for this collection:. C lick the Download button to save a ". TCIA encourages the community to publish your analyses of our datasets. Samples 70 Less SOFT formatted family file s. MINiML formatted family file s. Default this function will return a list include XenaHub object and selected datasets information. Once you are sure the datasets are exactly what you want, download can be set to TRUE to download the data.
This is for building consistent data download flow. Except destdir option, you only need to select three arguments for downloading data. Even throught the number is far less than getTCGAdata , it is more complex than the latter. Before you download data, you need spare some time to figure out what data type and file type available and what your datasets have.
Note not all datasets have these property, showTCGA can help you to check it. As most events occurred within 5 years, we used a time-dependent ROC curve to assess prognosis Fig.
To verify that our prognostic model can be applied universally, we further applied the seven-gene signature to ICGC data. A total of samples were obtained from the ICGC database, and after batch effect, samples remained. The five-year survival rate of patients in the high-risk group was low Fig. Internal testing cohort. ICGC validation cohort. The predictors in the nomogram included four independent prognostic factors age, gender, tumour stage, and race Fig.
The calibration curve illustrated that the predictions and actual observations matched well, which indicated an accurate prediction via the nomogram Fig.
Nomogram for predicting 3- and 5-year OS. We added up the points identified on the points scale for each variable that can be projected onto the scales to indicate the probability of 3- and 5-year OS. Calibration plot showing the prediction of OS. The nomogram-predicted probability of OS is plotted on the x-axis; actual OS is plotted on the y-axis. There is growing evidence that, despite the importance of individual molecules, tumorigenesis and prognosis are strictly controlled by interactions between a large number of cellular components including DNA, RNA, proteins, and small molecules [ 16 ].
However, the number of specific biomarkers with prognostic significance is still small [ 17 ], and the identification of prognostic factors is important for the optimal treatment of KIRC patients. We then conducted a GO enrichment analysis, showing that the DEGs are primarily involved in renal system development, kidney epithelium development, renal tubule development, and kidney development. After multivariate Cox regression with LASSO penalty, seven DEGs were identified, and two validation analyses were performed using independent datasets, showing good reproducibility.
The biological functions of the seven identified DEGs have been reported in previous studies. APOLD1 Apolipoprotein L Domain Containing 1 is an endothelial cell early response protein that may play an important role in the regulation of endothelial signalling pathways and vascular function.
C9orf66 Chromosome 9 Open Reading Frame 66 is a protein-coding gene. Any defects in this gene abrogate G6Pase function [ 18 , 19 , 20 ], which is associated with increased glycogen accumulation in gluconeogenic organs, especially in the kidneys, where it promotes progressive nephromegaly [ 21 ].
Poor metabolic control often results in long term complications such as renal dysfunction, pancreatitis, and hypertriglyceridemia, impairing kidney function and increasing the probability of KIRC [ 21 ]. The proteins encoded by this gene family are natural inhibitors of matrix metalloproteinases MMPs. In addition to its inhibitory role against most of the known MMPs, TIMP1 promotes cell proliferation in a wide range of cell types and may also have an anti-apoptotic function.
TUBB2B mutation leads to tubulin heterodimerization impairment, decreased ability to incorporate into the cytoskeleton, and alteration of microtubule dynamics, with an accelerated rate of depolymerization, which causes renal disease and an increase in the incidence of KIRC [ 24 ]. Compared to previous research, our study had some differences [ 25 , 26 ]. First, our risk score RS strategy involved LASSO penalized regression which can analyse all independent variables as well as the most influential variables.
When dealing with large datasets such as gene expression profiles, this method is much more accurate than the stepwise regression method of multivariate Cox regression models. We also acknowledge the limitations of this study. First, before clinical application, PCR-based sample validation should be conducted. Second, the functional phenotypes and mechanisms of the seven genes deserve further investigation. Our findings suggest that the seven-gene signature can serve as an independent biomarker for predicting survival prognosis, and we are poised for further investigation and eagerly anticipate the verification of our findings in a larger cohort of patients to assess whether the seven genes are likely to become new drug treatment targets.
After removal of the samples with inadequate clinical information, 99 KIRC and 74 normal control samples were selected for this analysis. KIRC clinical and gene expression data cases were downloaded from the TCGA database, and a total of cases ware obtained after removing the batch effect.
This study strictly followed the published guidelines issued by TCGA. The TCGA data were randomly divided and used as a prognostic model training set and an internal testing set, and the ICGC data were used as an external validation set. Excluding unmatched genes, genes were available for analysis. The biological significance of the DEGs was explored using a GO term enrichment analysis of biological processes, cellular components, and molecular functions.
We randomly divided the TCGA samples with approximate ratio of and samples were set as the training set and samples were set as the internal testing set. In the training group, multivariate Cox proportional hazard regression analysis was performed on DEGs [ 29 , 30 ], followed by LASSO penalty to further screen out a group of independent prognostic candidate genes with the strongest predictive power [ 31 ]. All statistical analyses were conducted by R3. Kaplan-Meier curves were generated for survival rates of patients, with difference detection based on log-rank testing.
Specifically, survival curves were established in the training set, internal testing set and ICGC set. The predictive performance of the nomogram was evaluated by a calibration curve [ 15 ].
For all statistical analyses, a two-tailed P value less than 0.
0コメント