However, the low capture of mRNA material within individual cells often leads to inaccurate quantification of genetic material. Consequently, a significant amount of expression values are reported as missing, which are often referred to as dropouts. To overcome this challenge, we develop a novel imputation method, named single-cell Imputation via Subspace Regression (scISR), that can reliably recover the dropout values of scRNA-seq data. The scISR method first uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and then estimates the dropout values using a subspace regression model. Our comprehensive evaluation using 25 publicly available scRNA-seq datasets and various simulation scenarios against five state-of-the-art methods demonstrates that scISR is better than other imputation methods in recovering scRNA-seq expression profiles via imputation. scISR consistently improves the quality of cluster analysis regardless of dropout rates, normalization techniques, and quantification schemes. The source code of scISR can be found on GitHub at  By allowing us to monitor the biological processes at the single-cell resolution, single-cell technologies (scRNA-seq) have enabled new research directions in genomics and transcriptomics research. These include various atlas projects4 aiming at building the references of all cell types in model organisms, transcriptome landscape visualization in complex tissues6, inference of cell developmental trajectories7, and predicting cell spatial position8. Such comprehensive decomposition of complex tissues holds enormous potential in both basic research and clinical applications10.Bulk RNA sequencing (RNA-seq) has been the primary tool to study biological systems. Despite its popularity, bulk sequencing is unable to measure the heterogeneity inside complex tissues and cell-to-cell variability. Recently, advances in microfluidics and sequencing technologies have allowed us to measure the expression profiles of individual cells11. One of the challenges is that sequencing mRNA within individual cells requires artificial amplification of DNA materials, leading to disproportionate distortions of relative transcript abundance and gene expression. Another outstanding challenge is the “dropout” phenomenon where a gene is highly expressed in one cell but does not express at all in another cell12. These dropout events usually occur due to the limitation of sequencing technologies when only a small amount of starting mRNA in individual cells can be captured, leading to low sequencing depth and failed amplification14. Since downstream analyses of scRNA-seq heavily rely on the accuracy of expression measurement, it is crucial to impute the zero expression values introduced by the dropout phenomenon and sequencing errors.However, scRNA-seq data also comes with additional challenges15. Methods in this category include scImpute16, SAVER17, and BISCUIT18. scImpute uses a Gaussian distribution to model the actual expression and a Gamma distribution to model the dropout events. It estimates the model parameters and dropout values using the EM algorithm. Similarly, SAVER17 models read counts as a mixture of Poisson-Gamma distribution and then uses a Bayesian approach to estimate the true expression values. BISCUIT18 uses the Dirichlet process mixture model19 to perform data normalization, cells clustering, and dropouts imputation by simultaneously inferring clustering parameters, estimating technical variations , and learning co-expression structures of each cluster.There have been a number of computational methods developed to impute single-cell data. These imputation methods can be classified into two categories: i) model-based methods and ii) model-free methods. Methods in the first category model the data using a mixture of two different distributions: one distribution represents the actual gene expression while the other accounts for the dropout events. Next, they estimate the model parameters and true expression values using the Expectation-Maximization (EM) algorithm20, DrImpute21, scScope22, DCA23, and DeepImpute24. MAGIC imputes zero values using heat diffusion25. The method first computes the affinity matrix between cells using a Gaussian kernel and then constructs the Markov transition matrix by normalizing and smoothing the computed affinity matrix. Finally, the method multiplies the exponentiated Markov matrix with the original data to obtain the imputed data. DrImpute21 uses a cluster ensemble strategy and consensus clustering to separate data into groups of similar cells and then imputes missing data by averaging expression values of similar cells. The other three methods  rely on deep neural networks to denoise the data and to impute the missing values. scScope uses a recurrent network layer to iteratively impute the zero-valued entries while DeepImpute randomly splits genes into subsets and builds sub-neural networks to estimate the missing values. DCA, on the other hand, extends the standard autoencoder to account for sparse count data by incorporating a noise model into their loss function.Methods in the second category typically assume that expression values from the same dataset follow a certain data structure (manifold), whereas dropout events move the values away from the underlying structure. These methods use regression techniques to infer missing values from genes or cells that have similar expression patterns. Methods in this category include MAGICThe quality of data imputed by methods in the first category (model-based methods) is determined by the validity of the assumption of the distribution models. In addition, these methods usually require excessive computational power, which makes them slow in processing big datasets. Therefore, these statistical methods often rely on gene filtering steps to ease the computational burden. For methods in the second category (model-free approaches), their major drawbacks include i) relying on many parameters to fine-tune their models, which can lead to overfitting, and ii) tending to over-smoothen and remove the cell-to-cell stochasticity that represents meaningful biological variations in gene expression. More importantly, in addition to the limitations mentioned above, methods in both categories attempt to alter the expression of all zero-valued entries, including those not affected by dropout events. This may introduce false signals and further weaken their reliability.26 to automatically group genes with similar patterns into smaller groups. The third module imputes missing values affected by dropout events (identified in the first module) by learning the gene patterns in each gene group (identified in the second module). This strategy ensures that the true missing values are imputed by using only highly relevant information. In an extensive analysis using simulation and 25 real scRNA-seq datasets, we demonstrate that scISR improves the quality of clustering analysis of single-cell data while preserving the transcriptome landscape.Here we propose a new approach, scISR, that can reliably impute missing values from single-cell data. Our method consists of three modules. The first module performs hypothesis testing to identify the values that are likely to be impacted by the dropout events. By not altering the true zero values, we can avoid false imputations. The second module utilizes a data perturbation technique27. Finally, in the third module, we estimate the missing values in the imputable data using the identified gene subspaces . The description of the datasets is shown in Table https://hemberg-lab.github.io/scRNA.seq.datasets). There are 14 plate-based datasets and 11 droplet-based datasets. Among these, 12 datasets are with UMI, and 13 datasets are with read counts. There are 7 datasets without normalization while the remaining 18 datasets were already normalized by the data providers: 3 CPM-, 3 TPM-, 4 RPKM-, 4 FPKM-, and 4 RPM-normalized.To assess the performance of the six imputation methods, we downloaded 25 publicly available scRNA-seq datasets available on NCBI, ArrayExpress, and Broad Institute Single Cell Portal . We also remove genes that do not contribute to the analysis, including: (i) genes expressed in less than two cells; and (ii) genes that have less than one percent of non-zero-valued entries. In all 25 single-cell datasets, the cell types are known. However, these cell labels are not provided to any of the imputation methods. They are only used 52, Jaccard Index (JI)53, and Purity Index (PI)54.We use the known cell types of the 25 scRNA-seq datasets to assess whether the imputation helps separate cells of different types in cluster analysis. We compare scISR against MAGIC, scImpute, SAVER, scScope, and scGNN using three assessment metrics: Adjusted Rand Index (ARI)k as the number of clusters. We calculate the Adjusted Rand Index (ARI)52 to compare k-means partitioning against the known cell labels. Rand Index (RI) measures the agreement between a given clustering and the ground truth. The ARI is the corrected-for-chance version of the RI. The ARI takes values from − 1 to 1, with the ARI expected to be 1 for a perfect agreement, and 0 for random partitionings. Next, we apply each of the six imputation methods to the raw data to obtain the imputed data. Again, we use k-means to partition the imputed data and calculate the ARI values using the true cell labels. We expect that by imputing the raw data, we obtain better data in which the cells of different types are more separable. Therefore, we assess the performance of each method by comparing the ARI of the imputed data against the ARI obtained from the raw data. We repeat the whole procedure for all 25 datasets to assess how well each imputation method performs.Given a dataset (raw data), we use k-means to cluster the cells using the true number of cell types Table 53 and Purity Index (PI)54. The detailed results for each dataset and method are reported in Table To perform a more comprehensive analysis, we also compare the methods using two other metrics: Jaccard Index (JI)8. This method can automatically determine the number of cell types from the input data. We first used Seurat to cluster the raw and imputed data of the 25 real scRNA-seq datasets. We then compared the clustering results against true cell types using Adjusted Rand Index (ARI). Supplementary Figure To further assess the performance of imputation methods, we perform an additional clustering analysis using SeuratNext, to assess the performance of each method with respect to different cell isolation techniques, quantitative schemes, and normalized units, we divide the datasets into multiple overlapping groups: (1) 14 plate-based and 11 droplet-based datasets; 2) 12 with UMI and 13 with read count; and (3) 7 without normalization, 11 with transcript length-normalization (RPKM/FPKM/TPM), and 7 with transcript-depth normalization (CPM/RPM). Fig.  12 with To understand the impact of data scaling on the performance of the imputation methods, we also perform the same analysis without log transformation applied to the input data. Supplementary Figure 55 and UMAP5. We will also quantify the similarity between the imputed and original landscapes using the distance correlation index56.The purpose of this analysis is to assess whether the imputation alters the transcriptome landscape. Preferably, life scientists impute the data in order to improve the quality of downstream analyses. At the same time, imputation should not completely change the data because of falsely introduced signals, leading to wrong or compromised findings. In the above sections, we have demonstrated that scISR significantly improves the quality of downstream analyses . In this section, we will demonstrate that scISR preserves the transcriptome landscape of the data as well. For this purpose, we will visualize the transcriptome landscape of the raw and imputed data using t-SNE55 to generate the 2D transcriptome landscapes of the raw and imputed data. The 2D visualizations of the 25 datasets are shown in Supplementary Figures First, we use t-SNE5. The visualizations are shown in Supplementary Figures To perform a more comprehensive analysis, we also generate the 2D transcriptome landscapes of the 25 datasets using UMAPdCor)56 for each imputed landscape generated by t-SNE and UMAP. Given X and Y as the 2D representations of the raw and imputed data, dCor is calculated as dCov is the distance covariance between X and Y while dVar(X) and dVar(Y) are distance variances of X and Y. Specifically, the method first calculates the pair-wise distances for X by computing the distance between each pair of cells, resulting in a square matrix. Second, it calculates the pair-wise distances for Y. Finally, it compares the two matrices using the formula described above to obtain the distance correlation. The dCor coefficient takes a value between 0 and 1, with the dCor is expected to be 1 for a perfect similarity. In our analysis, when we rotate the transcriptome landscape, dCor does not change. In contrast to Pearson correlation, this metric measures both the linear and nonlinear associations between X and Y56.To quantify the similarity between the imputed and original landscapes, we calculate the distance correlation index  uniform dropout distribution, (2) normal dropout distribution, (3) highly correlated cell groups, and (4) Splatter-based simulationIn the first scenario, we generate 6 datasets by varying the number of cells from 100 to 10,000 and the number of genes from 300 to 10,000. The cells/genes combination setups are presented as follows: 100complete data and use the expression values as the ground truth for benchmarking. Next, we introduce the dropout events. We randomly select masked data.In each of the 6 datasets, the expression values follow a normal distribution The case studies for datasets with 100, 1000, and 10,000 cells are shown in Supplementary Figures complete and masked data. scImpute improves the quality of the data but is still not able to separate some cell types. In addition, scImpute also alters the values of non-zero entries to make the data better fit into the assumed mixture model. SAVER further improves the transcriptome landscape and separates the 4 cell types. However, data imputed by SAVER does not entirely match with the complete data, in which many dropout values remain uncorrected many other dropout entries imputed with wrong values. scScope and scGNN oversmooth the imputed data such that it merges all the cells in four types together. The heatmaps clearly show that many expression values, including non-zero-valued entries, were altered by scScope and scGNN.These case studies show that MAGIC imputes the missing values by smoothing the expression values. Many expression values, including non-zero-valued entries, were altered by MAGIC, making the landscape of the imputed data very different from those of both Using the true expression values of the complete data in all 6 datasets, we calculate the mean absolute error (MAE) and correlation between the imputed data and the ground truth for the genes that were impacted by dropout events. Figure In the second scenario, we generate in total 40 datasets resulted from the combination of 2 different dropout distributions: uniform and normal, 4 different dropout rates: 60%, 70%, 80%, and 90%, and 5 different sizes of data with the number of cellscomplete data. Next, we randomly assign dropout values to the data in two different cases. In the first case, the dropout probability is uniformly distributed. In the second case, the dropout probability follows a normal distribution. For example, at 60% dropout rate, the dropout probability follows a distribution of N. We then vary the dropout rate from 60% to 90%. We name the data with dropouts as masked data. Next, we impute the masked data using imputation methods to obtain the imputed data. Finally, to assess the performance of imputation methods, we compare the imputed data against the complete data using Mean Absolute Error (MAE) and correlation coefficients. The detailed results are presented in Supplementary Figure To generate datasets of a certain size . Supplementary Figure dropout.mid parameters (midpoint parameter for dropout logistic function of Splatter). We set dropout.mid to 2.5, 3, 3.5, 4, and 4.5, which led to sparsity levels of 84%, 87%, 89%, 91%, 93%, respectively.In the last scenario, we perform additional simulations with negative binomial distribution as the noise model using Splatter. We set the number of genes to 15,000 and the number of cell types to 3. We generated 30 datasets with different cell numbers: 5000, 10,000, 25,000, 50,000, 100,000 and 200,000. For each sample size, we varied the sparsity levels by adjusting the We used the mean absolute error (MAE) values and correlation coefficients between the ground truth expression and imputed expression data to assess the performance of imputation methods. Supplementary Figure 60.In this work, we introduced a new method to mitigate the effects of dropout events that frequently happen during the sequencing process of individual cells. The contribution is two-fold. First, by introducing a hypothesis testing procedure, we avoid altering true zero values. Second, the subspace regression provides a more accurate imputation by limiting the imputation to gene groups with similar expression patterns. We compared our approach with state-of-the-art methods using 25 real scRNA-seq datasets and 116 simulated datasets. We demonstrated that scISR outperforms other imputation methods in improving the quality of clustering analysis. At the same time, we also demonstrated that scISR preserves the transcriptome landscape of each dataset. Finally, we showed that scISR is robust against different dropout rates and distributions. We expect that scISR will be a very useful method that can improve the quality of single-cell data. The tool can be seamlessly incorporated into other single-cell analysis pipelinesThis section describes the first module in scISR which aims at determining whether each zero value observed is the result of dropouts. Our hypothesis is that dropout events happen randomly for a gene affected by this phenomenon. By treating each cell as an instance of the population, we also assume that the ratio of zero values (dropout probability) reported for each cell differ from each other. Using dropout probabilities from both genes and cells, we can calculate how likely each zero values is affected by dropout. If zero values caused by dropout are over-represented in a gene, we conclude that this gene is affected by dropout events.XBin. Therefore, the conditional distribution of X, x is the number of observed zero values in the gene and r is the total number of observed zero values in the selected pair of gene and cell. The probability mass function of the hyper-geometric distribution can be written as follows:Given a zero-valued entry, let us denote X and Y have an overlapping entry for each gene and cell pair. Therefore, we remove the overlapping entry from the hypergeometic formula by using: i) n) as the number of measured values for the gene, and iii) x) as the number of zero values observed in the gene.Note that Applying Eq. , we calc27 that we recently developed. The method is based on the observation that small changes in quantitative assays will be inherently presented even when there is no significant difference between genes. If distinct gene groups do exist, they must be stable with respect to small degrees of data perturbation. This is indeed the case, as we have demonstrated in our previous work that the pair-wise connectivity between data points of the same group is preserved when the data are perturbed.It is crucial that the missing values of a gene are inferred using related genes that share similar expression patterns. Therefore, this module aims at identifying gene groups of the training data, i.e., gene subspaces that share similar patterns. For this purpose, we utilize the perturbation clusteringWe will describe this approach using an illustrative example shown in Fig. Assuming that we do not know the number of classes in this dataset, we set k. This resilience of pair-wise connectivity occurs consistently regardless of the clustering algorithm being used , or the distribution of the data. When there are no truly distinct subgroups, the connectivity is randomly distributed. When the number of true classes changes, the perturbed connectivity always reflects the true structure of the data.The perturbed connectivity matrices suggest that there are three distinct classes of genes. This demonstrates that for truly distinct gene groups the true connectivity between genes within each class is recovered when the data is perturbed, no matter how we set the value of 27. We choose the partitioning with the highest AUC and then partition the genes into subgroups that are strongly connected in those perturbation scenarios. We note that the idea of determining subspaces can be realized for both genes and cells simultaneously. We do not focus on such simultaneous clustering in this manuscript, but it is of great interest.To identify the optimal partitioning, we calculate the absolute difference between the original and the perturbed connectivity matrices and compute the empirical cumulative distribution functions of the entries of the difference matrix (CDF-DM). In the ideal case of perfectly stable clusters, the original and perturbed connectivity matrices are identical, yielding a difference matrix of 0s, a CDF-DM that jumps from 0 to 1 at the origin, and an area under the curve (AUC) of 1I in which all of the genes are likely to be affected by dropouts (imputable set), and ii) a set T that have accurate gene expression that does not need to impute (training set). In the second module, we segregate T into smaller groups of genes (gene subspaces) that share similar expression patterns. In this third module, we will impute dropout values in group I using a generalized linear regression model on gene subspaces.In the first module, we divide the genes into two sets: i) a set g, we train a generalized linear model using only highly-correlated genes within the assigned subspace in T. The linear regression process consists of two steps. The first step is to select genes from the training set that are highly correlated with the gene we need to impute. In the second step, we train the linear model using these highly correlated genes and then estimate the missing values58.Given a gene in the imputable set g, S as the gene subspace in T that g was assigned to, S; and y. We calculate the Pearson correlation between y and T with the highest correlation coefficients (see Supplementary Figure y is the outcome variable. In our implementation, we adopt the lm function that is available in the stats R package. Next, we use the trained linear model to estimate the missing values in Denoting Supplementary Information.


MAGI2-AS3 lncRNA was found to be shared in almost all GI cancers. Moreover, the most common shared mRNAs between GI cancers were MEIS1, PPP1R3C, ADAMTSL3, RIPOR2, and MYLK. For each cancer ceRNA network, we found that the expression level of a number of lncRNAs and mRNAs was specific. Furthermore, our study provided compelling evidence that several genes, most notably KDELC1, can act as novel proto-oncogenes in cancers. This, in turn, can highlight their role as new prognostic and therapeutic targets. Moreover, we found cell cycle and extracellular matrix structural constituent as the top shared KEGG and molecular function, respectively, among GI cancers. Our study revealed several known lncRNAs and known and unknown mRNAs in GI cancers with diagnostic and prognostic values.Gastrointestinal (GI) cancers are responsible for approximately half of cancer-related deaths, highlighting the need for the identification of distinct and common features in their clinicopathological characteristics. Long ncRNA (lncRNAs), which are involved in competitive endogenous RNA (ceRNA) networks with critical roles in biological processes, constitute a substantial number of non-coding RNAs. Therefore, our study aimed to investigate the similarities and differences in the ceRNA networks of The Cancer Genome Atlas (TCGA)-GI cancers. We performed a comprehensive bioinformatics analysis of ceRNA networks for TCGA-GI cancers in terms of the deferential mRNA, lncRNA, and miRNA expression levels, ceRNA networks, overall survival analysis, correlation analysis, pathological cancer stages, and gene set enrichment analysis. Our study revealed several common and distinct mRNAs and lncRNAs with prognostic values in these networks. It was specifically noteworthy that  The gastrointestinal (GI) system consists of the substantial cellular mass in the human body, and its cancers are among the most common malignancies in different populations, accounting for 35% of the total deaths due to cancers . Over thMore than 90% of the mammalian genome is transcribed into non-coding RNA (ncRNA), with a considerable number of them consisting of long ncRNAs (lncRNAs), transcripts over 200 nucleotides (nt) long . IncreasHere, we report different and common shared lncRNAs and mRNAs involved in the ceRNA networks of these cancers. Moreover, our study shows the common and specific pathways in these cancers. Our finding also shows that a combination of lncRNAs and mRNA with prognostic values can promote their use for diagnostic and therapeutic aims. Furthermore, we propose a number of mRNAs that can function as new proto-oncogenes or tumor suppressors in their corresponding cancers, highlighting their diagnostic and therapeutic aspects.Since our study reveals common shared and distinct mRNAs, lncRNAs, miRNAs, KEGG, GO-MF, GO-BP, and GO-CC among GI cancers, the identified results can help other researchers to use the data for further functional studies and differential diagnostic and prognostic strategies.https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) contains raw data of genomic, epigenomic, transcriptomic, and proteomic experiments from over 20,000 primary tumor tissues and their matched normal counterparts from 33 cancer types. In this study, we used an R/Bioconductor package, GDCRNATool developed by Ruidong Li et al. (The Cancer Genome Atlas (TCGA)  that was shown to be upregulated in all cancers . After confirmation of consistency between our results and theirs, we performed our analyses for GI cancers. So, we confirmed that this method of analysis is reproducible for all TCGA cancers. In our study, several analyses were carried out using GDCRNATools, which included differential gene expression analysis, univariate survival analysis, competing endogenous RNA network analysis, and functional enrichment analysis.The appropriate data of these GI tumors and their matched adjacent non-tumor tissues were extracted. We started our analysis with the following sample numbers of RNA sequencing, miRNA, and clinical data for each cancer: COAD: 521 mRNA data, 465 miRNA data, and 459 clinical data; ESCA: 173 mRNA data, 200 miRNA data, and 185 clinical data; HNSC: 546 mRNA data, 569 miRNA data, and 528 clinical data; LIHC: 424 mRNA data, 425 miRNA data, and 377 clinical data; READ: 177 mRNA data, 165 miRNA data, and 171 clinical data; STAD: 407 mRNA data, 491 miRNA data, and 443 clinical data.In this package, we downloaded RNA sequencing, mature miRNA, and clinical data from TCGA. Then, we parsed RNA sequencing metadata, filtered duplicated samples in this RNA metadata, and filtered non-primary tumor and non-solid tissue normal samples in them. Then, we parsed miRNAs metadata, filtered duplicated samples in these miRNAs data, and filtered non-primary tumor and non-solid tissue normal samples in the miRNAs metadata. Next, we separately merged each RNA sequencing, miRNAs, and clinical data. After that, we normalized RNA sequencing data and miRNAs data . We then used the gdcDEAnalysis function to have a whole differential expression and the gdcDEReport function for differentiation expression of each mirRNA, lncRNA, and mRNA. Using the gdcCEAnalysis function considering data of differential expression of lncRNA and protein-coding genes, and extracting data of Starbas tool, we could decipher miRNA–lncRNA and miRNA–mRNA interactions and ceRNA networks of each GI cancer.limma. Low-expression genes with logcpm less than one were excluded from the analysis. In this R package, the gdcDEAnalysis function, which included limma, edgeR, and DESeq2, was used for the identification of differentially expressed genes (DEGs) and miRNAs between primary GI tumors and their matched normal tissues. Visualization methods were volcano, scatter, and bubble plots, and also three simple shiny apps were used in GDCRNATools. All the figures were plotted using the ggplot2 package included in this tool.We used this package to have the gdcParseMetadata function for the efficient organization of RNA and clinical data. With this tool, we applied the gdcFilterDuplicate function to remove duplicated samples and gdcFilterSampleType to filter out samples without primary tumors or matched normal counterparts. Raw counts data were normalized using the gdcVoomNormalization function, which applied the TMM method in edgeR and the voom method in Cytoscape 3.7.2.Using GDCRNATool, we investigated ceRNA networks in GI cancers. This tool with its gdcCEAnalysis function considers hypergeometric test (significantly commonly shared miRNAs between lncRNA and mRNA are detected), Pearson correlation analysis (lncRNA and mRNA with positive correlations are picked up), regulation similarity analysis (commonly shared miRNAs with similar function for regulation of the lncRNA and mRNA are considered), and sensitivity Pearson partial correlation to construct ceRNA networks. Using this tool, we first extracted several miRNAs that were correlated among both the lncRNAs and mRNAs. Then, this tool identified lncRNAs and mRNAs with positive expression correlation (Pearson correlation coefficient). Finally, we gained miRNAs with similar regulative effects on lncRNAs and mRNAs. In our analysis, we applied lncTarget (to get miRNA–lncRNA interactions) and pcTarget (to consider miRNA–mRNA interactions) data along with gdcCEAnalysis function to extract miRNA–target interactions predicted from several datasets. GdcCEAnalysis function considers data of miRNA–mRNA and miRNA–lncRNA interaction databases, for example, the interaction between miRNA and mRNA and also mRNA predicted by online datasets such as Starbas. In our analysis, lncRNA–miRNA–mRNA interactions extracted as edges and nodes were visualized in To find the main mRNA in each ceRNA network of GI cancers, using GDCRNA package with gdcCEAnalysis function, we also investigated lncRNAs–mRNAs correlation.We performed univariate survival analysis using the gdcSurvivalAnalysis function in the GDCRNA package with the selection of Kaplan–Meier (KM) analysis. In this analysis, the patients were divided into high- and low-expression groups according to the median.http://gepia2.cancer-pku.cn/#index)  .https://string-db.org) (We used the STRING database (-db.org)  to analyGDCRNATool, we investigated the role of the genes that were found to be involved in the GI cancers, in biological processes (BP), molecular functions (MF), and cellular components (CC). Using gdcEnrichAnalysis, we also carried out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).Using In the current study, we found different numbers of up- and downregulated mRNAs, lncRNAs, and miRNAs in HNSC, ESCA, STAD, COAD, READ, and LIHC given in Our study revealed the following numbers of differentially expressed mRNAs (DEmRNAs), lncRNA (DElncRNA), and miRNAs (DemiRNAs) maintained in the ceRNA networks of each cancer : HNSC 6], STAD .MAGI2-AS3 lncRNA to be shared among almost all ceRNA networks of GI cancers, except for ESCA, indicating the involvement of the same pathways in GI cancers  and found the presence of MAGI2-AS3, GAS5, SNHG1, KCNQ1OT1, SNHG20, and MIR17HG in both cancers. We were also able to distinguish COAD and READ using differential expression of H19, OR2A1-AS1, and MALAT1 (specific for COAD) and SNHG15, AC015813.1, and CCDC183-AS1 (specific for READ)  .SNHG12, AC005154.1, CCDC18-AS1, and SNHG7 were specific for LIHC; SNHG3, TMEM161B-AS1, and AC093010.3 were specific for ESCA; SNHG15, AC015813.1, and CCDC183-AS1 were specific for READ; C1RL-AS1, LINC00707, and LINC00958 were specific for HNSC; OR2A1-AS1 and MALAT1 were specific for COAD , and ADAMTSL3 and MYLK among CC-2   . Moreovend LIHC) , which iLINC00958 and LINC00707 in HNSC and overexpression of SNHG20 and SNHG12 in LIHC were correlated with reduced OS and worse prognosis. However, overexpression of SNHG20 in READ and PVT1 in STAD and low expression of MAGI2-AS3 in STAD were correlated with good prognosis (KDELC1 (POGLUT2) in HNSC, downregulation of RIPOR2 and SMOC1, and upregulation of TMEM164, B3GNT5, ZNF607, and ANKRD13B in LIHC. These genes showed reduced OS with worse prognosis. The downregulated genes with reduced OS and worse prognosis may function as tumor suppressors, but those overexpressed mRNAs with reduced OS and worse prognosis can act as proto-oncogenes.In the GI-ceRNA networks, we identified that the overexpression of rognosis . MoreoveSNHG20 lncRNA and BUB1 mRNA, and also SNHG12 lncRNA and TMEM164 mRNA (associated with reduced OS with worse prognosis), could strengthen the predictive value of these markers for this cancer. Each of the lncRNA–mRNA combinations was in the same ceRNA network.It worth noting that for LIHC, combinations of prognostic values of SNHG12 and SNHG20 in LIHC , PLXNA1 (p = 0.00176), E2F8 (p = 0.0155), EFNA3 (p = 3.74e−07), SLC7A11 (p = 0.0497), TMEM164 (p = 0.00469), H2AFZ (p = 5.27e−06), BUB1 (p = 5.82e−06), CBX2 (p = 0.00033), EXO1 (p = 4.88e−06), FANCE (p = 0.000301), LMNB2 (p = 0.000311), SOX12 (p = 0.00467), JPT1 (p = 4.08e−07), MAFG (p = 0.00305), STK39 (p = 0.0428), ANKRD13B (p = 0.000412)  .RIPOR2, SMOC1, B3GNT5, ANKRD13B, and TMEM164 has not been clearly demonstrated in LIHC. Moreover, the role of ZNF607 and mainly KDELC1 has not been clearly delineated in any cancer [in-silico analysis by KDELC1 in cancers as follows: One—Our data showed its over-expression with worse prognosis (reduced OS) in HNSC. Two—It showed overexpression in several tumors such COAD, kidney renal clear cell carcinoma (KIRC), and kidney renal papillary cell carcinoma (KIRP) and lower expression in Kidney Chromophobe (KICH) (https://www.genecards.org and https://www.proteinatlas.org). Four—It specifically targets extracellular EGF repeats of important proteins such as NOTCH1 and NOTCH3  . Three—Id NOTCH3 , indicat ; SNHG20 in READ; and PVT1 and MAGI2-AS3 in STAD (correlated with good prognosis). We found that over-expression of SNHG12 and SNHG20 in LIHC , which were not previously reported or supported by functional studies in their corresponding human tumors as follows: over-expression of KDELC1 (POGLUT2), in cancers since it is mainly localized in nucleus and endoplasmic reticulum, it specifically targets extracellular EGF repeats of important proteins involved in Notch signaling pathways, it has interactions with several main proteins involved in cancers, and it is upregulated in several TCGA cancers. Moreover, our study revealed several mRNAs associated with worse prognosis in cancer and showed their significant altered expression across tumor stages.According to the findings of the current study, we strongly proposed a novel proto-oncogene, SNHG20 lncRNA and BUB1 mRNA, and also SNHG12 lncRNA and TMEM164 mRNA (associated with reduced OS with worse prognosis), could be used as collective prognostic values for this cancer.Our study found that in ceRNA networks of LIHC, combination of prognostic values of In this study, we found extracellular matrix structural constituent as the top shared MF term, collagen-containing extracellular matrix as the top shared CC, and Cell cycle (hsa04110) as the top shared KEGG among all these GI cancers. However, extracellular matrix organization, except for LIHC, and regulation of chromosome segregation and mitotic nuclear division, both except for READ, were found to be the most significantly shared BP terms among the GI cancers.MAGI2-AS3 lncRNA in almost all GI cancers. Another interesting result was the identification of the most common shared mRNAs in these cancers. Moreover, our study revealed a number of specific lncRNAs and mRNAs in GI cancers. Furthermore, our study reported several genes, most notably KDELC1, that can have proto-oncogenic roles in cancers. In addition to the abovementioned findings, we reported the top shared KEGG, GO-MF, GO-BP, and GO-CC among GI cancers. Overall, our study showed that bioinformatic studies regarding mRNA-miRNA–lncRNA networks of TCGA data can help identify prognostic biomarkers. The identified common shared and distinct mRNAs, lncRNAs, miRNAs, KEGG, and GO-terms among GI cancers can be used by other researchers for further functional experiments and also for diagnostic and prognostic approaches.In conclusion, we found several common and distinct prognostic mRNAs and lncRNAs in GI cancers. The most important finding was the involvement of


The treatment of collecting duct carcinoma (CDC) remains unsatisfactory since this highly aggressive kidney cancer has an unfavorable clinical behavior. Due to the rarity of CDC a complete biological characterization of this disease is still lacking. The aim of our study is to provide new insights into the molecular biology of CDC leveraging newly generated and publicly available gene expression profiles of CDC tumors. We identified unique gene expression programs and pathways that distinguished CDC from other renal malignancies. The CDC-specific expression signature predicted in vitro sensitivity to several small molecule inhibitors screened in cancer cell lines from multiple cancer types. Finally, we proved that CDC is a molecularly heterogeneous disease made up of at least two subtypes distinguished by cell signaling, metabolic and immune-related alterations. Altogether, these findings pave the way for future investigations with meaningful clinical implications aimed at improving the management of CDC patients.Collecting duct carcinoma (CDC) is a rare and highly aggressive kidney cancer subtype with poor prognosis and no standard treatments. To date, only a few studies have examined the transcriptomic portrait of CDC. Through integration of multiple datasets, we compared CDC to normal tissue, upper-tract urothelial carcinomas, and other renal cancers, including clear cell, papillary, and chromophobe histologies. Association between CDC gene expression signatures and in vitro drug sensitivity data was evaluated using the Cancer Therapeutic Response Portal, Genomics of Drug Sensitivity in Cancer datasets, and connectivity map. We identified a CDC-specific gene signature that predicted in vitro sensitivity to different targeted agents and was associated to worse outcome in clear cell renal cell carcinoma. We showed that CDC are transcriptionally related to the principal cells of the collecting ducts providing evidence that this tumor originates from this normal kidney cell type. Finally, we proved that CDC is a molecularly heterogeneous disease composed of at least two subtypes distinguished by cell signaling, metabolic and immune-related alterations. Our findings elucidate the molecular features of CDC providing novel biological and clinical insights. The identification of distinct CDC subtypes and their transcriptomic traits provides the rationale for patient stratification and alternative therapeutic approaches. Collecting ducts carcinoma (CDC) is a rare and aggressive histological subtype of renal cancer accounting for less than 1% of renal tumors. This malignant epithelial tumor arises from the distal segment of the collecting ducts of Bellini in the renal medulla, bearing distinct clinical, histological and pathological characteristics ,3,4. CDCTo date, the treatment of metastatic disease remains highly unsatisfactory and, given the rarity of CDC, only a few data are available to support the best therapeutic options for this aggressive malignancy. One of the major findings in terms of overall response rate was achieved in a prospective phase II trial showing the efficacy of the chemotherapy combination cisplatin plus gemcitabine in 23 previously untreated metastatic CDC (mCDC) patients . DespiteNF2, SETD2 and SMARCB1 genes with 29%, 24% and 18% frequency, respectively [CDKN2A gene and a recurrent mutation in the MLL gene in about 50% of cases [MLL and SMARCB1 mutations, all other somatic mutations were already reported in ccRCC [SLC7A11, a cisplatin-resistance marker [Only a few studies have examined the molecular profile of CDC. Targeted sequencing performed on 17 CDC cases led to the identification of recurrent somatic mutations in ectively . Anotherof cases . With thin ccRCC . Transcre marker . An indee marker . Anothere marker . Furthere marker . Comparie marker .Even though these studies have provided useful knowledge about CDC biology, the small sample size of the analyzed cohorts hinders the general validity of their findings. Thus, the definition of pathognomonic transcriptional features of CDC is still lacking. In our study we applied an integrative analytical approach to robustly elucidate the molecular traits of this rare tumor type. Our results show that CDC tumors are characterized by a distinctive transcriptional program that recapitulates gene expression alterations linked to both cancer-related processes and cell of origin of the tumor. For the first time, we provide indication that CDC transcriptome may be predictive of sensitivity to several drugs in cancer cell lines, and we highlight the existence of at least two molecular subtypes of CDC driven by marked transcriptional differences.Tumor samples from 6 CDC patients were collected at Fondazione IRCCS Istituto Nazionale dei Tumori of Milan . Histological verification of specimens was performed by an expert uropathologist using established criteria for the diagnosis of CDC . All patThe collection of specimens and associated clinical data used in this study were approved by the Independent Ethics Committee of the Fondazione IRCCS Istituto Nazionale dei Tumori of Milan (Protocol number INT 31/14). The study was approved by the Institutional Review Board of The Ohio State University  (Protocol number 2005C0014). All patients provided written inform consent to donating the tissue remaining after diagnostic procedures for analysis. Tissue collection and usage were performed in accordance with the ethical standards established in the 1964 Declaration of Helsinki and its later amendments. The privacy rights of human subjects have been observed.Total RNA was extracted from formalin-fixed, paraffin embedded samples using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE , quantified on a Qubit fluorometer  and assessed for integrity on a 4200 TapeStation, . Gene expression profiles were generated using GeneChip Human Transcriptome Array 2.0 microarrays (Thermo Fisher). RNA labeling, processing, and hybridization were performed according to manufacturer’s instructions, and microarrays were scanned with the GeneChip System 3000 (Thermo Fisher) scanner. Quality controls were performed on raw and preprocessed data to control for batch effects and outliers.Additional transcriptomic data of CDC and normal samples were downloaded from the Gene Expression Omnibus (GEO) repository (raw RNA-Seq counts of GSE89122 ). A thirRNA-Seq fastq files for renal medullary carcinoma (RMC)  were obtRNA-Seq raw counts for ccRCC patients included in the International Cancer Genome Consortium—Renal Cell Cancer—European Union/France (ICGC-RECA-EU) dataset were downloaded from the ICGC data portal    were dowhttps://portals.broadinstitute.org/ccle (accessed on 14 October 2020)). Pharmacological data were obtained from the https://www.cancerrxgene.org/downloads/bulk_download (accessed on 14 October 2020)) [For the Cancer Cell Line Encyclopedia (CCLE) , RNA-Seqr 2020)) , respectMicroarray raw data were preprocessed using appropriate methods for each specific platform. Raw Affymetrix CEL files were preprocessed using the frozen robust multi-array average (RMA) algorithm  from BioSTAR  was applp-values were corrected for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) [Differential expression analysis of microarray datasets was carried out using the standard linear modeling approach implemented in the limma package . For RNAte (FDR) . Differete (FDR) . Genes wFor scRNA-Seq data, genes differentially expressed between cell types were identified using the FindMarkers function of the Seurat package  with Wilp-value < 0.05 and were grouped in functional categories using the following network approach. We first measured pairwise gene content similarity between terms by computing the Cohen’s kappa statistic as previously described [https://www.genenames.org (accessed on 4 November 2020)) [t-statistic derived from the differential expression analysis with limma. The number of permutations was set to 10,000 and gene sets with less than 15 genes or more than 500 genes were filtered out. Significant gene sets were selected according to an FDR < 0.05. Reactome gene sets were manually grouped into functional categories according to the Reactome pathway hierarchy. Inference of immune cell infiltration from gene expression data was carried out using the ESTIMATE algorithm [Over-representation analysis of gene lists was performed with the gProfiler2 package  with defescribed . Term paescribed . Term clescribed  and manur 2020)) . Gene Ser 2020))  was perfr 2020)) . Genes wlgorithm .To compare CDC with other RCC histologies and normal kidney, we combined the INT-CDC dataset with all other datasets reported in To compute similarity between bulk and single-cell expression data we first standardized expression values by calculating the z-score across samples for each gene. We then correlated bulk and single-cell expression data using Pearson’s correlation coefficient, after selection of the top 5000 most variant genes in single-cell data with the FindVariableFeatures of Seurat package . FinallyTo increase the number of CDC samples for unsupervised analysis, we first merged processed microarray data and log2(FPKM) values of the four datasets of CDC and normal kidney samples ) was interrogated using the list of 31 genes of the INT-CDC signature. From the results, we selected the connectivity scores for 10 drugs of interest, calculated for the 9 cancer cell lines  profiled within the Touchstone database [The Connectivity Map database   in at least one of the three public CDC datasets  in all three CDC datasets. This INT-CDC signature was enriched in genes involved in extracellular matrix organization, cell–cell communication, and epithelium development  specifically upregulated of at least two-fold in CDC compared to the other samples . Of notedatasets . Furtherelopment . We evalelopment B. RMC shGiven the poor prognosis of CDC, we hypothesized that the INT-CDC signature could be associated to high tumor aggressiveness. Due to the small sample size and the lack of follow-up data, the prognostic impact of the signature was not evaluable in CDC datasets. Therefore, we assessed the association of the INT-CDC signature with prognosis using transcriptomic and survival data of ccRCC, papillary, and chromophobe RCC patients of TCGA. Increased expression of the INT-CDC signature was significantly associated to poor overall survival and progression-free interval in ccRCC patients . No assoAmong pathways significantly over-represented in CDC versus normal samples were those involved in immune response, immune signaling and interferon-γ signaling  and highGenes whose expression is modulated in tumor cells compared to the normal counterpart are useful for the screening of candidate therapeutic targets and for the development of novel drugs. To identify compounds that may be active in CDC we correlated the CDC transcriptional program with drug sensitivity in 1019 cell lines of CTRP and 971 cell lines of GDSC, for which both transcriptomic and pharmacological data are available. The CDC transcriptional program was defined by the score of the INT-CDC signature, calculated for each cell line using singscore. The INT-CDC signature score was finally correlated with drug response data, expressed as AUC, to identify positive and negative associations. Correlation analysis in CTRP dataset identified 28 compounds, among the 481 tested, which were significantly negatively correlated (FDR < 0.05) with the INT-CDC signature . This meGene expression profiles of CTRP and GDSC cell lines were obtained from treatment-naïve cells, therefore all associations with drug sensitivity data were evaluable in a predictive setting. Assuming that the INT-CDC signature is a molecular proxy of a gene expression profile of a highly aggressive tumor type, we interrogated the Connectivity Map database to assess whether the 10 drugs negatively associated to the INT-CDC signature could actually revert its expression. The Connectivity Map provides positive scores if the perturbational signatures in the database are positively correlated with the query signature. Negative Connectivity Map scores indicate dissimilarity between perturbational and query signatures. All drugs except for bosutinib and lapatinib showed negative scores, supporting the evidence that the perturbational gene expression signatures of these drugs are negatively correlated with the INT-CDC signature D. TherefCDC has a presumed origin from the principal cells of the distal collecting ducts of Bellini. To confirm this hypothesis from a molecular perspective we compared, at single-cell level, the expression profiles of bulk CDC samples and other renal cancer histologies to scRNA-Seq data obtained from normal kidney . CorrelaTo obtain a broader picture of the specific transcriptional features underlying CDC biology, we compared expression profiles of CDC to other renal cancer histologies , UTUC, and normal kidney. To reduce the heterogeneity of the different datasets related to sample composition and profiling technology, the comparison was performed at the level of single-sample scores for Reactome pathways. We observed the highest number of differentially expressed gene sets between CDC and chromophobe tumors A. The con = 17). Given the low sample size and the difficulty in identifying robust clusters we applied COLA, an approach that enables the identification of consensus clusters combining different gene variability measures and partitioning algorithms. The tested 24 methodological combinations led to the identification of two major consensus clusters, CDC-S1 and CDC-S2 (n = 8) and CDC-S2 (n = 9) to explore the molecular differences underlying these two subtypes. The INT-CDC signature was equally expressed in the two clusters, indicating that its expression is not affected by intertumor heterogeneity , 25% of cases diagnosed previously as CDC were recently reclassified as FH-deficient RCC . A similPrevious studies, all including only from two to seven CDC cases, identified a large number of genes differentially expressed between CDC and normal kidney but none of them evaluated the validity of their findings in independent datasets ,14,15. HConversely, in our analyses downregulated SLC genes could potentially play a role as tumor suppressors . BesidesPrevious CDC gene expression studies were limited to the comparison between tumor and normal tissue or UTUC samples. For the first time, we carried out a comprehensive comparison of CDC and all the other RCC histologies and UTUC to identify pathognomonic features of this tumor type.We identified a signature of 31 CDC-specific genes (the INT-CDC signature) whose expression was higher in CDC than other RCC histologies and UTUC, except for RMC that showed comparable levels with CDC. CDC and other RCC histologies and UTUC were found to be distinct tumor entities also at the pathway level, but less marked differences were highlighted between CDC and RMC. Future investigations will require orthogonal approaches, such as real-time qPCR or immunohistochemistry, in larger cohorts that include CDC and other kidney cancer to further explore the specificity of the INT-CDC signature as well as similarities with RMC. Such validation will ultimately lead to its refinement and to the development of a molecular diagnostic tool that may help clinicians to identify CDC. CDC and RMC were reported to be similar also in terms of clinical, histological features and anatomical site of origin . Our datThe identification of processes uniquely enriched or depleted in CDC might also provide therapeutic options for patients. CDC-specific upregulated features included DNA repair, innate immune system, and Rho GTPases. Rho GTPases are master regulators of actomyosin structure and dynamics and play pivotal roles in a variety of cellular processes including cell morphology, motility, adhesion, metabolism, survival, proliferation and cell cycle, stress, inflammation, gene transcription, and apoptosis . Small-mThrough the analysis of cancer cell line pharmacogenomics datasets, we showed that the INT-CDC signature was also predictive of higher sensitivity to tyrosine kinase inhibitors (TKIs), including EGFR, ABL, and IGFR signaling inhibitors, and to SRC and MAPK signaling inhibitors. Assessment of the translational value of such in silico drug sensitivity predictions will require derivation of well characterized CDC patients’ cell lines or primary cultures to be used for preclinical screening of anticancer drugs.In vitro, the combination of saracatinib, a SRC kinase inhibitor, and the TKI sunitinib, was proven to inhibit proliferation and migration in RCCs . MetastaAs far as immunotherapy is concerned there is anecdotal evidence for the efficacy of checkpoint inhibitors in CDC patients ,9,10. ByHeterogeneous treatment responses can be related to molecular heterogeneity. Our data give the first indication that at least two CDC subtypes exist. Even if some of the cases retrieved from the literature may have been misdiagnosed, the equal distribution of samples of the INT-cohort in the two clusters indicates that these subtypes are not related to the presence of different tumor types. The two subtypes showed large differences in many biological pathways including metabolism, cell cycle, DNA repair, signaling pathways, and tumor immune microenvironment. Classification of CDC in molecular subtypes may have prognostic and predictive implications. We found that the CDC-S2 subtype is similar to the TCGA-KIRC m1 subtype that was associated to better survival in ccRCC patients . CDC-S1 Overall, this study expands the previous knowledge and provide novel insights into the biological processes that define this rare RCC histology. Although we collected and integrated all CDC expression data publicly available so far to increase robustness and statistical power, we recognize that the sample size is still limited. In addition, we are aware that analysis of data from multiple sources and platforms introduces batch effects that may have a detrimental impact on the results by masking important signals in the data. Despite such limitations, we identified, for the first time, significant pathways discriminating CDC tumors not only from normal cells but even from other RCC cancer histologies, derived a gene signature upregulated in CDC that allowed in-silico predictions for new therapeutic options, discussed the role of overexpressed molecular targets and key signaling pathways as new therapeutic targets. We also provide the first evidence for the existence of two molecular subtypes of CDC guided by different gene signatures, whose validation in larger homogeneous datasets might lead to more tailored and effective therapies for this cancer type with urgent unmet clinical need.


IDH wild type, with molecular features of glioblastoma” by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for “  •SUMO performs joint factorization of patient-similarity networks to determine subtypes•SUMO can efficiently handle missing or incomplete data and technical noise•SUMO uses consensus clustering to assign labels and can handle various data types•SUMO identifies a subtype of LGG with a differential prognosis and GBM-like features High-throughput experimental methods generate big high-dimensional (multi-omics) datasets. Subtyping samples/individuals on multiple data types has the potential to reveal systems-level insights and thus facilitates personal treatment of cancers and other genetic diseases. To overcome limitations in handling missing data and technical noise appearing in such datasets, we present a robust approach for integrative subtyping of multi-omics data that can extract biologically meaningful subtypes and handle noisy inputs and missing data without imputation. Sienkiewicz et al. present a computational method that performs joint factorization of multiple patient similarity networks to identify molecular subtypes of a disease. It can handle noisy and missing data effectively without imputation. The method consistently identifies subgroups with significantly different prognoses and enrichment of clinical associations on many datasets. Biotechnologies for large-scale molecular studies of genetic diseases have advanced significantly. High-throughput assays are now available to measure RNA expression, DNA methylation, and metabolite concentration in tissues. Given that each assay reveals a snapshot of certain cellular aspects of a disease, integrative analyses are often necessary for a complete understanding of its molecular etiology and for discovering its molecular subtypes and biomarkers .Molecular typing through clustering has traditionally focused on gene expression. In studies with multiple data types, a popular strategy is to concatenate feature matrices from the various data types and then operate on the resulting matrix. This approach allows use of existing clustering techniques but requires cross-data type normalization and feature selection in individual data types before concatenation, possibly biasing the results. More sophisticated methods  that can integrate continuous data from multiple data types to infer molecular subtypes. SUMO effectively handles missing data and produces robust clusters by using a resampling-based approach  and showcase an implementation called SUMO . Diffuse low-grade and intermediate-grade gliomas together make up the LGGs , a diverse group of primary brain tumors with highly variable clinical behavior. Several studies have associated l course . We idenl course . The remWe performed several simulations to compare the performance of the various methods on noisy datasets by varying the data noise level and the fraction of missing data that were generated by a procedure given in Using the same setup, we compared SUMO with NEMO regarding their ability to accurately classify samples with missing data. Other methods do not handle missing data and so were not included in this comparison. In this experiment, we removed a random fraction of samples from one data type while preserving the data in the other data type. SUMO shows a higher median ARI compared with NEMO for most data points B.We compared SUMO with several other methods using a recently published benchmark . The benlog10 p = 18.88), with MCCA being the second best with 17.48. However, the sum of p values can be biased because of outliers, so we also counted the number of datasets for which a method’s solution obtains significantly different survival (p < 0.05)  B. As withttps://github.com/ratan-lab/sumo_analysis.SUMO outperformed the other approaches in this benchmark, finding at least one cluster with significantly different survival in 7 of the 10 cancers analyzed. For colorectal cancer and lung squamous cell carcinoma, none of the methods identified a subtype that showed significant differential survival. SUMO is the only method to find a subgroup of individuals with ovarian cancer with a significant differential survival B. This gχ2 test used to assess the enrichment of clinical parameters. PINSPlus results varied significantly in terms of survival and enrichment of clinical labels. It is likely that most of these methods, with the exception of NEMO, CIMLR, and iClusterBayes, would benefit from a resampling approach similar to that used by SUMO to generate more consistent subgroups in the data.All methods with the exception of iClusterBayes identified at least one cluster in glioblastoma (GBM) with a significantly differential prognosis. We used these GBM data to investigate the reproducibility and robustness of the methods; i.e., whether sample labels, the p values for the log rank test, or the number of enriched clinical parameters change if we changed the seed to the random number generator used by the methods and the assessment calculations. We ran each method 10 times using random seeds and found that the methods were stable to different extents on these data C. NEMO gThe benchmark from For each cancer, we again chose or calculated parameters for the methods, as suggested by the authors, without considering the survival parameters. We then used a log rank test to assess whether at least one of the subtypes showed significantly differential survival. A log rank test was performed using overall survival (OS) for all TCGA cancers except diffuse large B cell lymphoma (DLBC), testicular germ cell tumors (TCGT), thymoma (THYM), and pheochromocytoma and paraganglioma cancer (PCPG). Based on the recommendations by log10 p = 89.8) and the best median prognostic value (−log10 p = 1.44) among the methods compared in this analysis  in 19 cancers, which is the most among the compared methods, followed by NEMO, which finds prognostic clusters in 17 cancers A. All meanalysis B. For nip values C when thWe also used the gene expression data for each of the 34 cancers with PROGENy to calculate the pathway activity scores for 11 signaling pathways. We report the p value of the Kruskal-Wallis test to determine whether the pathway is differentially activated in at least one of the subtypes determined by SUMO in IDH mutation status to separate IDH mutant astrocytoma and oligodendroglioma from IDH wild type astrocytoma, which has a worse prognosis (IDH status as the primary driver of two macro-clusters (IDH mutant gliomas were further composed of three coherent subgroups: (1) the Codel group, consisting of LGGs with 1p/19q codeletion; (2) the G-CIMP-low group, including gliomas without 1p/19q codeletion with relatively low genome-wide DNA methylation; and (3) the G-CIMP-high group, including gliomas without 1p/19q codeletion with higher global levels of DNA methylation. They also concluded that the IDH wild type gliomas segregated into three subgroups: (1) classic-like, exhibiting a classical gene expression signature; (2) mesenchymal-like, enriched for mesenchymal subtype tumors; and (3) pilocytic astrocytoma (PA)-like, enriched for tumors with molecular similarity to grade I PA.The 2016 World Health Organization (WHO) classification of diffuse gliomas recommends use of rognosis . Severalrognosis  to integclusters . The autThe cIMPACT-NOW  initiative was established to evaluate and recommend changes to future CNS tumor classifications based on consensus review of novel diagnostically relevant data . In seveIDH mutant LGGs with 1p/19q codeletion and the majority of the IDH mutant LGG without 1p/19q codeletion with higher global levels of DNA methylation. IDH wild type and who were significantly older at the age of diagnosis . Subtype 2 is also enriched for grade III tumors  and significantly enriched for anaplastic astrocytomas (p < 10−5); it is also enriched for samples with a high percentage of aneuploidy .EGFR) oncogene on chr7. Somatic aberrations in the EGFR, including amplification and activating point mutations, occur in ∼57% of grade IV gliomas but are relatively uncommon in LGGs (EGFR) and chr10 loss, which leads to deletion of the PTEN gene, a known tumor suppressor. According to the WHO guidelines from 2016, chr7 gain and/or chr10 loss are not considered in the diagnosis of grade II/III gliomas, although other studies have suggested that these events are clinically relevant. Recent recommendations from the cIMPACT-NOW consortium suggest that EGFR amplification and combined chr7 gain and chr10 loss as well as TERT promoter mutation can be used to diagnose IDH wild type (WT) grade II/III gliomas that are likely to follow a more aggressive clinical disease course , mutations in the TERT promoter and high expression of TERT , high tumor purity , 1p/9q codeletion, and mutations in CIC, a known tumor suppressor. 128 of the 130 individuals in subtype 1 have a methylated promoter for MGMT . MGMT promoter methylation is associated with a better response to alkylating chemotherapy, suggesting that individuals assigned to subtype 1 are more likely to respond to temozolomide  and shows a higher enrichment for mast cells  subtype detected in previous gene expression studies. The NE subtype has been related previously to the tumor margin, where increased normal NE tissue is likely to be detected . Consistst cells , which ast cells .We present an approach to integrate multi-omics datasets using the NMF of patient-similarity networks. Similar to other similarity-based methods, such as SNF, we first transform information from each data type into a separate patient-similarity network, which allows us to preserve and handle data-type-specific properties. We then use a joint factorization to calculate a shared representation of the samples in a lower-dimension subspace. Our implementation, SUMO, additionally enforces sparsity on this representation, making it well suited for unsupervised learning. Furthermore, we use a resampling technique in conjunction with consensus clustering to detect the optimal number of clusters and assess the stability of the generated clusters. Our validation experiments show that this resampling strategy has the potential to improve the output from other subtyping methods as well.χ2 approximation are highly inaccurate in evaluating clustering solutions on real cancer datasets  samples with a poor prognosis were assigned to the same cluster as the PA-like samples based on the CpG methylation markers, highlighting that tumor grade provided prognostic value independent of subtype and age. We found that PA-like samples from Ceccarelli et al. (2016) are classified by SUMO primarily into two different groups based on gene expression and miRNA expression. The assignment correlates with the tumor grade, with most grade III tumors being assigned to subtype 2, which is characterized by a poor clinical course. This example highlights the need for integration of clinical observations and outcomes with molecular information in determination of clinical subtypes. Even though we use our approach to cluster samples, additional constraints can be used to adapt the approach for semi-supervised applications, as suggested in other studies using NMF  classified a subset of sing NMF .CLCF1 are the best markers for the subtype. SUMO is available as a Python package, which includes modules to construct patient-similarity networks and infer molecular subtypes. A common post hoc analysis of molecular subtyping is identification of features that can be used as markers or surrogates for the various subtypes. SUMO includes a mode to build a tree-based model that can predict the importance of each feature for each of the detected subtypes. For example, we identified a clinically relevant subtype of LGG with a differential prognosis compared with the other subtypes. According to our analysis, the non-CpG island methylation probes in proximity to the gene In this study, we compare SUMO with several methods that integrate continuous genomic data types to discover biologically relevant molecular subtypes. The metrics used in these assessments have their limitations and can reflect adversely on the performance of methods with additional objectives beyond determination of subtypes. Even though we can integrate categorical and ordinal data types using SUMO, we did not benchmark SUMO against the other methods for those data types. The current implementation of SUMO has a few limitations. For example, it can be slower than competing methods for large datasets because it uses consensus clustering of multiple NMF decompositions to assign the final labels. Even though the current implementation can train a gradient boosting classifier to identify features that can act as biomarkers for the assigned clusters, the implementation requires large amounts of memory.ratan@virginia.edu).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Aakrosh Ratan ). We then calculated the median ARI of the classification at each data point for 100 repetitions for each method compared in the experiment. The scripts to produce the simulated dataset and compare the various methods are available at https://github.com/ratan-lab/sumo_analysis.https://github.com/ratan-lab/sumo_analysis.To simulate missing information, we removed a random fraction of samples from one of the two data types selected at random while keeping corresponding sample data in the other data type. We again calculated the median ARI of classification at each data point for 100 repetitions for each method compared in the experiment. The scripts to produce the missing dataset and compare the various methods are available at https://github.com/ratan-lab/sumo_analysis.We compared SUMO to several other methods using a recently published benchmark . The scrThe NMF technique aims to explain the observed data using a small number of basis components by factoring the data into the product of two non-negative matrices; one representing the basis components, and the other containing mixture coefficients . NMF hasn samples, the similarity between samples based on the ith data type forms a n × n symmetric matrix Ai. After that, we tri-factorize Ai ≈ HSiHT, where H is a non-negative n × r matrix, Si is a r × r non-negative matrix, and r (≪n) is the desired number of clusters. H in this decomposition is shared among the various data types and is a representation of the n samples in a r-dimensional subspace accounting for the adjacencies observed in all data types. Each row of H represents a sample, whereas each column of H denotes a cluster. We include an additional constraint to enforce sparsity of H in the factorization.Similar to NEMO and SNF, we preprocess, transform, and standardize the data before calculating the similarity between the samples for each data type separately. If all data types are measured for all Lastly, we use multiplicative updates to solve the above factorization. Since the solution can be sensitive to the initial conditions and the input data, we run the solver multiple times on several subsets of samples using different initial conditions and use consensus clustering to assign the final labels and infer the optimal number of clusters . We descData preprocessing involves (a) filtration, (b) transformation, and (c) normalization of each data type separately. The filtering process removes features that are not informative; for example, we remove genes with zero counts in most samples. Although our approach can handle missing values, removing features and samples with a large fraction of missing values (>10%) often speeds up computation and is recommended unless it removes a significant fraction of samples.The transformation process is data-dependent. For instance, we use a variance-stabilizing transform to convert abundance in count data (as in RNA-seq) to yield a matrix of values that are approximately homoscedastic . This transformation has the additional advantage of reducing the effect of outliers in the data. In the case of methylation data, we use of log2 ratio of the methylated to unmethylated count, also referred to as M-values . If batcIn the normalization step, we perform feature standardization to make the value of each feature in the data be zero-mean and unit variance. Our data-preprocessing is similar to other similarity-based methods such as SNF, but we do not require imputation to fill in missing data, which can be computationally intensive for genome-wide datasets.n be the number of patient samples s that are found in the dataset of every data type and let t be the number of data types . In this step, we construct a similarity network N, represented by a set of n×n similarity matrices k is used as an index for the data type. aij(k) represents the similarity between two samples si and sj calculated from the features of the kth data type, Let k, we assume its data is represented in a matrix (fij) containing n sample rows and p feature columns. We calculate Ak as a radial basis function of the Euclidean distance xi and xj:μ is a hyperparameter and εi represents the average distance between xi and its K nearest neighbors NK(i):μ equal to 0.5 based on performance on simulated datasets and set the number of nearest neighbors K equal to 10% of the samples in the data type. The selection of K and μ change the emphasis on the local and global structure in the similarity graph and can affect the results. In μ and higher values of K both increase the number of pairs with lower values in the similarity matrix. Varying these parameters does not have a significant effect when SUMO is applied to well-separated simulated datasets with a known number of clusters, even with large amounts of missing data or noise  = 1.The Euclidean distance is appropriate for normalized count datasets, such as for gene expression and DNA methylation data. However, depending on the data type and the application, different distances or similarity metrics may better represent sample relationships. For example, cosine similarity has been shown to be a better metric for the calculation of similarity between single cells in the single-cell sequencing for transposase accessible chromatin (scATAC-seq) . CurrentAi of the multiplex network N is symmetric and non-negative. We tri-factorize H is a n × r matrix shared across the data types and r is the desired number of clusters such that Each matrix that r≪n B.H and Si are both constrained to be non-negative. The first term of the objective function measures the divergences between Ai and HSiHT using the Frobenius norm in each data type. For each data type, measurements may be not available for all the n samples, thus leading to missing entries in the matrix Ai. We use Wi to remove the missing values, whereWe compute the above tri-factorization by minimizing the following objective function:ni is the number of samples for the ith data type.Then we add another factor H, and the hyperparameter η is used to balance the contribution of these two terms (see η selection).The second term of the objective function is used to enforce sparsity on the matrix H and Si. The following multiplicative updates are used to solve the optimization problem given in Note that the cost function in H and Si converge to a local minimum of the cost function. We apply the above rules iteratively while alternating fixed matrices, keeping track of objective function value ℒi) to the cluster corresponding to the column that contains the maximum value, as depicted in Since the solution is relatively sparse, we can assign each sample  represent the trace of matrix X. ThenThe corresponding Lagrange function isLet Si as follows:We obtain the update formula for H,B0 ≥ 0 is the Lagrange multiplier for H. Thus, H satisfies the following equations:Similarly, when we update matrix H:Then, we obtain the following update formulas for sumo prepare), tri-factorize the multiplex network to assign samples to the desired number of clusters (sumo run), compare the assignments to another classification using multiple metrics , and detect the importance of each feature towards each cluster (sumo interpret).SUMO is specifically designed to integrate multi-omic data for molecular subtyping. It consists of four subroutines. It allows the user to construct the multiplex network from normalized feature matrices (https://github.com/ratan-lab/sumo) and at The Python Package Index (https://pypi.org/project/python-sumo).SUMO is available in the form of a command-line tool on GitHub (NA (not available). A missing value in an adjacent matrix Ai is equivalent to a missing edge between two nodes in the multiplex network and is masked during factorization using Wi.Biomedical studies measure a large number of molecular parameters. Almost every dataset has missing entries. Most methods for molecular subtyping require complete data. This implies that both samples and features that have missing entries have to be removed or the missing entries have to be imputed in the preprocessing stage. SUMO takes a different approach. It scales the calculated distance between a pair of samples by the number of common features available for both samples. If sufficient overlap (by default at least 10% of features) is not found, the distance is set to η in cost function  denotes the sum of similarities for all the sample pairs in the identified cluster Ci given the similarity matrix Aj and nj is the number of samples for the jth data type. We then choose η which results in the highest sη.The hyperparameter function  is used H according to the average similarity matrix across all data types. This method reduces residual error and provides faster convergence than using random initialization. However, we still have to set Si randomly; as such, the algorithm does not guarantee convergence to a local minimum. Here, we set the diagonal entries of each Si to be absolute singular values, that are derived from the SVD decomposition of the corresponding Ai matrix. We repeat the factorization n times to avoid overfitting, each time including 95% of the total samples in calculating the cluster assignments from H and a residual error (RE) for that run. We create a consensus matrix C from these n assignments that is weighted to incorporate the RE of each factorization in a dataset with t data type as follows:Our solution using multiplicative rules can be sensitive to the initial conditions and the input data. Both initialization and convergence speeds are important factors to consider when formulating the appropriate factorization algorithms . Our metWe use the Normalized Cut clustering algorithm  on this Estimation of an optimal rank for NMF is a challenging problem. It is common to compare several solutions based on a clustering metric. We implement two popular metrics that leverage the consensus matrix to help the user in the determination of stable solutions to the factorization. The first metric is the cophenetic correlation coefficient (CCC). It measures the Pearson correlation between sample distances and its hierarchical clustering. A higher CCC value is considered better. The second metric is the proportion of ambiguously clustered pairs (PAC), which is defined as the proportion of the consensus matrix values in  range. Based on our experiments, we recommend investigating factorization rank values for which the PAC score is less than 0.1, and the CCC value is high . Increasing the number of repetitions of the solver can assist in the identification of the optimal number of clusters, but as we show in Once the subtypes are assigned, a frequent challenge is to identify a set of features that correlate with the cluster separation. These can be used as markers for the assignment of future samples and can aid in understanding the differences between the groups. To this end, we first train a gradient boosting classifier implemented in LightGBM . We use HSiHT while adding sparsity to the cost function to improve separability, and uses H to assign labels to the samples. When using a single data type, the decomposition of the similarity matrix can simply be done into HHT without the need for a data type specific Si. This formulation is equivalent to a Laplacian-based spectral clustering. So, for each data type, we converted the similarity matrix into the normalized Laplacian and determined the eigenvalues and the eigenvectors of the Laplacian. We then used the eigenvalues of the graph Laplacian and chose the number of clusters corresponding to the maximum drop-off. We finally used k-means on the matrix with the selected eigenvectors to determine the clusters based on the data type.Our approach decomposes similarity based on each data type into χ2 test for independence and enrichment for numeric parameters was calculated using the Kruskal-Wallis test. The p-values for clinical enrichment were corrected using Bonferroni correction, and adjusted p-values < 0.05 were considered to be statistically significant in All statistical analysis reported in this article were performed in R. The p-values for the log-rank test in


Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established.We develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four‐layer deep neural network, named DSnet‐v1, for sarcopenia diagnosis.http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co‐expression with other genes implied the potential existence of race‐specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided.Among isolated testing datasets, DSnet‐v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (Our new AI model, DSnet‐v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia. Ageing involves progressive changes in an individual's physiology. Sarcopenia, defined as muscle loss, is a leading cause of frailty in the elderlyThe pathogenesis of sarcopenia is associated with ageing, which may impede either anabolic signalling to build components of the muscle, regenerative activity to repair damaged tissues, intricate circuits to deliver nervous signals in and out of the muscle, or cellular surveillance to maintain energy flexibility of the body.In this study, we aimed to develop an artificial intelligence (AI) model for sarcopenia diagnosis using a previously published transcriptome dataset that contain differentially expressed genes in muscle biopsies from patients with sarcopenia and age‐matched healthy individuals across three ethnic groups. The AI model developed in this study yielded a diagnostic accuracy of >94%, which is an unprecedented level. To the best of our knowledge, our current study is the first attempt to develop an AI model to diagnose sarcopenia based on a transcriptome dataset only.https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE111017.TableDataWe used a published transcriptomic dataset deposited in the Gene Expression Omnibus of the National Center for Biotechnology .For hold‐out validation, we segmented the data into training (80%) and testing (20%) datasets in a stratified manner based on both the race  and outcome . Accordingly, we used 94 subjects as the training dataset  and 24 data as the testing dataset . The testing dataset was isolated and used only to evaluate the performance of the proposed AI model. For box or scatter plots, data are shown as median ± interquartile. *Supporting information, Figuremean(train) and SD(train) are the mean and standard deviation values, respectively, for each feature from the training dataset. Standardization was applied to both the training and testing datasets.In the dataset, each of the 17 339 genes can be the feature for the AI model. Some features (gene information) were missing from the training and testing datasets ,By repeating the five‐fold cross‐validation 10 times, we obtained the best hyperparameters. For AdaBoost, we set the hyperparameters as follows: number of tree estimators, 200; learning rate, 0.2. For the RF, we set the number of tree estimators to 100, maximum depth to four and maximum features to five. For XGBoost, we set the maximum depth to four, learning rate to 0.1, number of tree estimators to 100, regularization parameter α to 1.0, fraction of observations to 0.9 and fraction of columns to 0.9.Based on the resultant 50 sets of feature importance values for each classifier , we averaged the values and normalized them such that the importance values from each classifier ranged from zero to one. Next, we averaged the importance values for the final ranked feature importance values. Finally, we determined the optimal number of top features to be incorporated into the AI diagnosis model based on the cross‐validation results.A deep neural network (DNN) was used to develop the final AI model for sarcopenia diagnosis. In the DNN approach, we investigated up to five hidden layers, and each layer depth (node) up to the previous layer depth (node). For the input layer, we first ranked the features based on their importance and increased the number of top features used in the input layer. Accordingly, we used the top 27 features as input layers. For the fully connected (FC) layers as hidden layers, we applied dropouts by changing the dropout rate from 0 to 0.5, at increments of 0.1. The last FC layer was fed into a sigmoid layer, which is an output layer that provides the probabilities of patient severity. We trained the models using the ADAM optimizer and binary cross‐entropy cost function with a learning rate of 0.0001 and a batch size of 64.FigureFor each set of top features, we obtained the best cross‐validation accuracy using two metrics, that is, the area under the curve and the balanced accuracy Equation .(2)Ban Figure. The fouyi is the label  and p(yi) is the classified probability of each subject being sarcopenic for a batch size comprising N patients.We implemented and trained the DNN using TensorFlow (version: tensorflow‐gpu 2.0), whereas we used NumPy (version: 1.16.4), Pandas (version: 0.25.3), Matplotlib (version 3.1.2) and Scikit‐learn (version 0.22.1) to build the model and analyse the results. We trained the models using the ADAM optimizer and the binary cross‐entropy cost function shown in Equation To evaluate the performance of DSnet‐v1 for the diagnosis of sarcopenia, we used accuracy metrics of sensitivity, specificity, accuracy, balanced accuracy and area under the receiver operating characteristics (AUROC).In the training dataset, our DSnet‐v1 was evaluated based on 5‐fold stratified cross‐validation. Subsequently, the diagnostic performance was evaluated independently using an isolated testing dataset. To compare the performance of DSnet‐v1 with those of other external AI models, we separately trained the models of RF, XGBoost and AdaBoost, each of which was evaluated with hyperparameter search.https://www.r‐project.org). The network visualization of human phenotype ontology was assessed using Enrichr.The evaluation and visualization of gene expression (boxplots), Spearman's correlation (correlogram matrices), and gene network for 27 AI‐featured genes and their associated genes were conducted using R packages ggpubr, ggplot2, igraph, ggraph, egg, corrr, corrplot, dplyr, tidyverse, and reshape (http://sarcopeniaAI.site/) through Amazon Web Services (AWS), which provides secure, durable and scalable service. After accessing the website, a user enters the 27 genes, which are encoded to the website server and can immediately obtain the diagnosis result of sarcopenia. There is no need to enter any private information other than gene information, and the entered information is immediately deleted when the diagnosis result is derived, so there is no risk of information exposure.We deployed DSNet‐v1 on a public web server , DSnet‐v1 showed a sensitivity of 1.00, specificity of 0.94, accuracy of 0.96, balanced accuracy of 0.97, and AUROC of 0.99.  was elevated in the sarcopenic muscle, whereas 10 genes  showed reduced expression. The remaining 10 genes did not show statistical differences between patients with sarcopenia and age‐matched healthy individuals. Twenty‐seven AI‐featured genes were positively (blue) or negatively (red) correlated with each other (the depth of colour for Spearman's rho between 0.0 and 0.5)  in patients with sarcopenia (green box) and in age‐matched healthy individuals (white box) Figure. The gen) Figure.FigureFigureFigurehttps://hpo.jax.org/) assay revealed the physiological and pathological relevance of the three groups  to access the model. We believe that allowing the public to access the AI model will facilitate the validation and improvement of the model.Our proposed AI model, DSNet‐v1, successfully diagnosed sarcopenia accurately . This model presents several unique characteristics. First, our model was developed based on subjects across three different continents: Europe, Africa and Asia. Second, our AI diagnosis model was developed using a vast amount of gene information (i.e. 17 339 genes), and feature importance analysis was performed to identify genes associated with sarcopenia. Finally, we created a web application , RASSF1, PSMA6 and CRHR2 were changed only in the JSS, whereas CENPC, KAT2A, PEF1, VPS35L, H4C3 and SUMO1P3 were altered only in the SSS. Although it might imply that there is a race‐specific pathology in sarcopenia, the power (n) might be not enough to lead to a clear conclusion. In the case of PCK1, it failed to generate a statistical significance in the HSS. However, the mean expression was escalated. Because PCK1 is known to be expressed in only gluconeogenic or glyceroneogenic tissues such as the liver, kidney, intestine and fat cells , the altered expression of PCK1 might imply the anatomical loss of muscle and gain of adipocytes. Besides, Migliavacca et al. clearly demonstrated that gene sets associated with mitochondrial function and NAD+ biosynthesis pathway were tightly associated with the development/progression of sarcopenia.+ biosynthesis metabolism, several genes could be associated indirectly with two pathways (i.e. mitochondria and NAD+ biosynthesis). For instance, it is well demonstrated that NAD+ biosynthesis and mitochondria function are tightly associated with sirtuin, a NAD+‐dependent protein deacetylaseWe observed that the expression patterns of several genes varied among the three races Figure. For insy Figure. For insFigureUsing Spearman's correlation assay, we identified three functional groups that were highly associated with 27 AI‐featured genes Figure. InteresTablen = 24), which was a dataset segregated from an entire dataset. It may be necessary to validate our AI model using external datasets, such as prospectively collected data. In addition, we plan to further develop DSnet‐v1 and change its name to DSnet‐v2 and DSnet‐v3. To update the model, we will use our developed web application to acquire additional data and validate the model. Currently, the application does not store any information entered by users. However, we plan to store information entered by users upon agreement to improve the AI model via a real‐time learning process. Third, our data included only three subjects of different races. In future studies, we will train and apply our AI model to more datasets comprising more diverse subjects.Our study has several limitations. First, to find the 27 genes, we used the three machine learning algorithms such as RF, XGBoost and AdaBoost, which result in opposing feature importance values for some genes. To improve generalization performance, we used an ensemble approach, which combines machine learning techniques into one stable model by reducing variance and bias. To investigate the effectiveness, we compared the performance when the selected features are obtained from RF, XGBoost, AdaBoost and their ensemble in Our AI model with 27 selected genes diagnosed sarcopenia accurately. We believe that it can facilitate healthcare providers in treating patients with early‐stage sarcopenia.Heewon Chung, Yunju Jo, Dongryeol Ryu, Changwon Jeong, Seong‐Kyu Choe and Jinseok Lee declare that they have no conflict of interest.Figure S1. Histogram for the number of available gene information. 39 subjects include approximately 16,100 gene information out of 17,339. 40 subjects include approximately 16,600 gene information. 39 subjects include approximately 16,700 gene information.Figure S2. Receiver operating characteristic curves (ROCs); four different models of RF, XGBoost, AdaBoost, and DSnet‐v1based on testing data.Figure S3. Scatter plots showing the relative expression of each gene in the healthy and sarcopenic elderly in the three different races.Figure S4. Gene network showing co‐expression of group α, β, and γ. This figure is a replicate of Figure 6b and includes the name of each node (gene). The Spearman's Rho of two node generates the color and depth of each edge. The color  of node indicates each group .Table S1. Summary of training and testing datasets.Table S2. The values of sensitivity, specificity, accuracy and balanced accuracy according each different number of selected features.Table S3. Performance comparison based on the selected features from RF, XGBoost, AdaBoost and their ensemble.Click here for additional data file.Data S1. Gene information.Click here for additional data file.Data S2. Ranked Feature importance.Click here for additional data file.