Lymph node metastasis risk stratification is crucial for the surgical decision‐making of thyroid cancer. This study investigated whether the integrated gene profiling  of Fine‐Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer.In this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included. Multi‐omics data of FNA samples were assessed by an integrated array. To predict lymph node metastasis, we built models using gene expressions or mutations (SNV and fusion) only and an Integrated Risk Stratification (IRS) model combining genetic and clinical information. Blinded histopathology served as the reference standard. ROC curve and decision curve analysis was applied to evaluate the predictive models.p < 0.001), and the IRS model had similar predictive performance in large nodule  and small nodule  subgroups. The genetic risk factor showed independent predictive value  of lymph node metastasis in addition to the preoperative clinical information, including TIRADS grade, age, and nodule size.One hundred and thirty two patients with pathologically confirmed papillary thyroid cancer were included between 2016–2017. The IRS model demonstrated greater performance [AUC = 0.87 (0.80–0.94)] than either expression classifier [AUC = 0.67 (0.61–0.74)], mutation classifier [AUC = 0.61 (0.55–0.67)] or TIRADS score [AUC = 0.68 (0.62–0.74)] with statistical significance  model was developed to predict the risk of papillary thyroid cancer lymph node metastasis based on Fine‐Needle Aspiration samples' multi‐omics data. The IRS model achieved high AUC (0.87), sensitivity (0.86), and specificity (0.78), well calibrated, and improved clinical benefit in clinical decision analysis, thus helping make wise decisions preoperatively and reducing unnecessary extensive surgeries. When integrating large amounts and different types of information, these methods' limitations become apparent. Machine learning algorithms, such as random forest (RF) and artificial neural networks (ANN), can overcome their limitations and achieve superior prediction performance.This study was conducted to test the hypothesis that the integrated gene profiling of FNA samples and a predictive model constructed by machine learning method would improve the risk stratification of thyroid cancer, thereby guiding surgical decision‐making more effectively.22.1We performed a retrospective cohort study involving 132 thyroid cancer patients at Tianjin Medical University Cancer Hospital, China, between 2016–2017, who met the following eligibility criteria: (1) 18 years or older; (2) underwent total thyroidectomy/lobectomy and central lymph node dissection ; (3) had preoperative FNA samples with Bethesda grades 3 or 4; (4) pathologically confirmed thyroid cancer.FNA of the thyroid was performed routinely under ultrasound guidance. The ultrasound images were evaluated by an independent, experienced sonographer according to the Thyroid Imaging Reporting and Data System (TI‐RADS) published by the Committee of the American College of Radiology (Version 2017). Cervical lymphadenectomy was typically performed for lymph nodes according to the Guidelines for diagnosis and treatment of thyroid nodules and differentiated thyroid cancer . Cytopathology and histopathology were accessed by blinded experts. FNA samples and post‐surgery para‐nodule tissues of all patients were archived and accessed through gene profiling. Five patients were excluded because their samples did not meet the quality criteria.This study was approved by the institutional review board (IRB) of Tianjin Medical University Cancer Hospital. All participants provided written informed consent.2.2Extraction of DNA/RNA was performed with the Mag‐bind blood and tissue DNA HDQ 96 kit and Mag‐bind Total RNA 96 kit (Omega Bioservices), respectively. Reverse Transcription by the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). DNA/RNA purity was detected by a UV spectrophotometer (Nano Drop Technologies), and quantification was performed using the Qubit 3.0.The library was prepared using the Uthyroid TM panel, which consists of DNA, RNA fusion, and RNA expression libraries. After adding barcodes and adaptors for NextSeq (Illumina) using PCR reagents, the quality of PCR products was checked with LabChip GX Touch24 (PerkinElmer). The sequencing process was carried out according to Illumina's protocols (Illumina) using NextSeqCN500 (BerryGenomics) 300 cycle reagent cartridge corresponding to 2 × 150 bp paired‐end configuration. The average depth of each run was over 500×.2.3p‐value (FDR) and fold‐change were used for preliminarily feature selection, as shown in volcano plots  algorithm served as a powerful integration method by which all types of genetic features and preoperative clinical information were combined to obtain the Integrated Risk Stratification (IRS) model.k = 10) was used to evaluate their performance. The model development and evaluation were done with Python (version 3.6.2).In the end, we evaluated three predictive models we built: the expression classifier, mutation classifier, and the IRS model, and we also compared them with the TIRADS grade. For all the predictive models, k‐fold cross‐validation .Categorical data were summarized with frequencies and percentages, and continuous data were summarized with mean (standard deviation) or medians (interquartile ranges) as appropriate. The area under the curve (AUC) of the receiver's operative curve (ROC), sensitivity, and specificity were calculated using established methods. AUCs in this study were compared using Delong's method. Clinical usefulness and net benefit were estimated by decision curve analysis (DCA).33.1Clinical characteristics  of the 132 included patients with thyroid cancer were presented in Table 3.2p = 0.001). Other mutations were also more frequent in patients with lymph node metastasis, although without statistical significance.Table 3.3p < 0.001). The IRS model demonstrated greater performance  than either expression classifier , mutation classifier  and TIRADS score  with statistical significance (p < 0.001). The optimal diagnostic cut‐off of the IRS model was 0.5, yielding a sensitivity of 0.86 (CI: 0.78–0.89) and a specificity of 0.78 (CI: 0.72–0.84). The two genetic classifiers  had higher sensitivities but lower specificities compared with the TIRADS score, thus potentially having complementary predictive value. Figure Figure 3.4To further access the predictive value of gene profile and clinical information, we performed a multivariate logistic regression of the lymph node metastasis risk. As the expression and mutation classifier were strongly correlated, the linear model failed to include both of them as risk factors. Therefore, the genetic risk, a combination of these two classifiers, was used in the regression. The clinical characteristics, including age, sex, nodule size, TIRADS grade, and unifocal/multifocal, were considered potential risk factors. Table Furthermore, we listed the top 10 genetic features which contributed the most to the IRS prediction model regarding the relative importance score from the random forest Table  and the 3.5c, by which extensive surgery is only performed for patients with risk higher than pc. Each pc also corresponds to a cost/benefit ratio .To further assess the clinical impact on disease management, we applied decision curve analysis to our predictive model, as shown in Figure pc > 0.4 .Figure 4In the current clinical practice, the management strategy of papillary thyroid cancer varies in counties and hospitals. For instance, prophylactic central lymphadenectomy, for instance, remains controversial because it may reduce the likelihood of persistence/recurrence, but introduces additional risks of surgical complications.To the best of our knowledge, our study is the first to integrate FNA gene profiling  for lymph node metastasis risk stratification in papillary thyroid cancer. We conceptually demonstrated that multi‐omics prediction is superior to expression or mutation prediction. In this study, the expression, SNV, and fusion of FNA samples were obtained through sequencing on one integrated platform (Uthyroid TM panel), which would make the utilization of multi‐omics data in clinical practice more applicable.p > 0.2 . Thus, the IRS model is more likely to have a real impact on clinical decisions.The associations between genetic profile and lymph node metastasis have already been reported,Our study population contained a fair portion of small‐size thyroid cancer (PTMC), for which gene profiling of FNA samples is scantly studied. There is a doubt that the FNA for PTMC is remarkably imprecise.There are several limitations of our study. First, the sample size is limited, so we can only include a limited number of risk factors in our model to avoid over‐fitting, and our model was not externally validated. Second, our sample did not cover enough pathological diversity, all of our cases are PTC or PTMC. However, in the real clinical setting, histopathology is unavailable preoperatively. Thus the impact of patients with other pathological types should be considered when applying our model. Further studies with larger sample sizes and a more general population are required.5In this study, we developed an Integrated Risk Score (IRS) model predicting the risk of lymph node metastasis of papillary thyroid cancer based on multi‐omics data of FNA samples. The IRS model achieved high AUC (0.87), sensitivity (0.86), and specificity (0.78), well‐calibrated, and significantly improved clinical benefit in clinical decisions like prophylactic central lymphadenectomy. We conceptually proved the utility of integrating multi‐omics data and applying machine learning algorithms in this important clinical setting. Further study is required to validate this model in a larger and more diverse population.Weituo Zhang: Conceptualization ; data curation ; formal analysis ; funding acquisition ; writing – original draft ; writing – review and editing . Xinwei Yun: Conceptualization ; data curation ; formal analysis ; resources ; writing – review and editing . Tianyu Xu: Formal analysis ; software ; writing – review and editing . Xiaoqing Wang: Data curation ; writing – review and editing . Qiang Li: Formal analysis . Tiantian Zhang: Formal analysis . Li Xie: Writing – review and editing . Suna Wang: Software . dapeng Li: Data curation . Xi Wei: Conceptualization ; data curation ; resources ; writing – review and editing . Yang Yu: Conceptualization ; resources ; writing – review and editing . Biyun Qian: Conceptualization ; data curation ; funding acquisition ; methodology ; project administration ; resources ; writing – review and editing .The code supporting this study's findings is available from the corresponding authors upon reasonable request.The authors declare no competing interests.Appendix S1.Click here for additional data file.


This study presents a novel computational framework scFseCluster for scRNA-seq clustering analysis. Particularly, an excellent feature selection algorithm FSQSSA was encapsulated in the scFseCluster platform, which achieves optimal gene sets for clustering tasks. Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis. Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized biological research , 2. TradscRNA-seq clustering is a critical step for cell-type identification, helping to uncover hidden patterns and potential confounding factors , 13. HowOver the past few years, several computational methods for scRNA-seq clustering have been developed. These methods can be classified into two categories: (1) machine learning (ML)-based methods , 22, 23 In this study, we proposed a novel computational framework scFseCluster for scRNA-seq clustering analysis. With the support of Feature Selection based on Quantum Squirrel Search Algorithm (FSQSSA), a metaheuristics module for feature selection, scFseCluster implemented the clustering task on optimal gene sets and achieved excellent performance on a batch of benchmark datasets with different scales. Further analysis revealed that our proposed method is significantly superior to seven State-of-the-Art (SOTA) algorithms. In summary, scFseCluster is a promising model for accurately identifying cell types from the scRNA-seq data.Preprint), Romanov .To test the performance for gene selection, we applied the proposed FSQSSA on eight benchmarking scRNA-seq datasets , includi Romanov , Darmani Romanov , Usoskin Romanov , Monroto Romanov , and Hrv Romanov . These d Romanov . Then, w Romanov , Enhance Romanov , Artific Romanov , and Gen Romanov . As show Romanov . In partSupplemental Data 1. Performance of all feature subsets obtained from each dataset under parallel computing.Supplemental Data 2. Performance comparison of optimal feature subsets obtained by all five metaheuristic algorithms.To analyze the robustness of FSQSSA, we further evaluated its performance from the following two aspects. First, we checked if the convergence performance of FSQSSA was steady across multiple replicates. From Supplemental Data 3).To investigate the clustering performance, we applied scFseCluster to eight scRNA-seq datasets with known cell types to analyze its clustering performance. A total of six metrics  were used . As showSupplemental Data 3. Comparative analysis of clustering performance between scFseCluster and seven SOTA algorithms on eight scRNA-seq datasets.We also compared the performance of scFseCluster with seven existing methods, including Seurat , scDeepCTo illustrate the effectiveness of dimension reduction, we applied t-SNE to visualize the final embedded points in two-dimensional (2D) space, which were learned by the FSQSSA in the scFseCluster model. The 2D space representations of other methods are also plotted. In addition, we found that both scDeepCluster and DESC are better for clustering visualization on datasets with moderate sample size (PBMC and Romanov). However, they perform poorly on other datasets. In summary, our scFseCluster model provides the best clustering visualization compared with seven other SOTA algorithms.Supplemental Data 4). For most methods, the optimal genes selected by FSQSSA provided better clustering performance. Across all datasets, ARI values were increase by an average of 0.11 and ACC by an average of 0.09 for the first six SOTA methods, indicating a significant improvement in the clustering performance of these methods. In some datasets, clustering performance of some methods improved significantly with the specified gene subsets. For example, Seurat’s ARI value increased from 0.24 to 0.75 and the ACC value from 0.41 to 0.81 for clustering using the optimal gene set of FSQSSA features from the PBMC dataset. All of the above tests were well-controlled. The only difference between the blue and red bars is the feature selection procedure. Although these different clustering tools perform differently on different datasets, using features selected by FSQSSA can improve their clustering accuracy. Taken together, we show the superior performance and broad applicability of FSQSSA, regardless of the clustering methods, experimental protocols, and dataset size.Unlike the experiments shown above, in this study, we applied the optimal gene sets generated by our model to seven other clustering methods to demonstrate if feature selection is an essential step in scRNA-seq clustering analysis. Supplemental Data 4. The impact of using or not using feature selection on clustering performance metrics (ARI and ACC).Driven by a meta-heuristic method (FSQSSA), the computational cost of scFseCluster is valuable to be investigated. In this study, we examined the running time of scFseCluster and other comparative algorithms on all the datasets. Clustering and cell-type identification are important steps in scRNA-seq data analysis. In this study, we proposed a novel computational framework scFseCluster for scRNA-seq data clustering analysis. Particularly, an excellent feature selection algorithm FSQSSA was encapsulated in the scFseCluster platform, which achieves optimal gene sets for clustering tasks. We tested scFseCluster on many scRNA-seq datasets from different species (mouse and human) and tissues  and demonstrated that scFseCluster is capable of providing steady and precise cell clustering.Compared with the other seven well-known SOTA methods, scFseCluster achieved higher clustering performance on datasets of various scales. As a wrapper strategy , FSQSSA Table S1.The number of markers inferred by Seurat, and the overlaps with the Top 50 genes in FSQSSA's replicates.Overall, the major contributions of this work are as follows. First, our proposed feature selection model is more general because the wrapper-based model is easier to select the optimal subset of features. Moreover, the dimensionality of the data can be significantly reduced (around 852 genes) by using FSQSSA, which greatly saves the time overhead for downstream analysis. Finally, this study suggests that researchers should mainly focus on gene selection rather than clustering models when performing scRNA-seq clustering analysis.Limitations still exist in the current study. Swarm intelligence algorithms are stochastic in nature, and multiple runs can lead to inconsistent final results. To guarantee strong robustness, each dataset in this study was run 500 times in parallel. Another disadvantage is that the number of clusters position  or Louvaposition  to deteruse Mojo  to reconWe collected six publicly available scRNA-seq datasets containing cell-type annotations and gene expression values from various scRNA-seq platforms, which can be downloaded from the Gene Expression Omnibus  and BioSAssume that analysis . Therefo package   to the objective problem. As a multi-objective problem, FSQSSA tries simultaneously minimizing the size of a subset of selected features and maximizing the clustering accuracy of a given subset of features. Based on the above basis, the fitness function constructed to achieve a balance between the two objectives for determining the solution, in this case, is defined as shown in ur study , 72, 73.As mentioned earlier, three types of trees in the forest represent different food resource classes. To make FSQSSA achieve a better balance between exploration and exploitation, we assume that there are 50 trees in the forest; only 1 tree was top-ranked hickory, 3 second-ranked acorns, and 46 lowest-ranked normal trees. The number of squirrels matches the number of trees in the forest, with only one squirrel per tree.Squirrels need to constantly search for more advanced resources in the forest to satisfy their requirements. The dynamic foraging process of a flying squirrel leads to three scenarios: (1) a squirrel flies from an acorn tree to a hickory tree; (2) a squirrel flies from a normal tree to an acorn tree; and (3) a squirrel flies from a normal tree directly to a hickory tree. It is hypothesized that in the absence of natural predators, a squirrel glides throughout the forest and effectively searches for food, whereas the presence of natural predators causes it to become alarmed and forced to flee to random locations. Natural enemies give each squirrel room to escape, which makes FSQSSA less likely to fall into a local optimum solution. We define the probability of the presence of a natural enemy as A squirrel flies from an acorn tree First, calculate the seasonal constant ((b)Check the seasonal monitoring condition, that is, (c)If seasonal monitoring condition is found to be true, those flying squirrels unable to explore the forest for optimal winter food sources will be randomly relocated. Because the normal trees have the lowest level of food resources, FSQSSA assumes that only squirrels in the normal trees will be forced to migrate randomly in search of better food sources. The random migration of squirrels is given by Seasonal changes can significantly affect the foraging activity of squirrels . They suo phases .(c)If segorithms . Le´vy fRI and We aggregate six quality metrics  includinparisons . NMI andignments . H(U) and recall . The defTo prove the effectiveness, we carried out a comparison analysis from two aspects. In one aspect, we compared FSQSSA with metaheuristic methods, including the standard Squirrel Search Algorithm (Squirrel) , EnhanceIn another aspect, we compared our scFseCluster algorithm with seven SOTA methods for scRNA-seq data clustering, which includes two deep learning approaches scDeepCluster  and DESCTable S2.The information of seven comparative methods for scRNA-seq clustering.The codes of scFseCluster were developed and debugged by using Tensorflow 2.7.0 and Python 3.7.0 under the environment with GPU 3090 and 32G RAM. All the simulations and parallel calculations (500 repeats) were performed on NJAU-HPC with 16G memory of graphics card.https://github.com/wzqwtt/scFseClusterV1.0. All the processed data are placed in the link: http://cdsic.njau.edu.cn/data/scFseClusterV1.0. The researchers can also download the data and source code from FigShare: https://figshare.com/s/a0c082d8244c942b5515.The source code of scFseCluster framework is available at GitHub:


Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more biological insight as compared to conventional gene-based analysis. In this article, we propose a framework combining genetic data into pathway structure and using an ensemble of convolutional neural networks (CNNs) along with a Canonical Correlation Regularizer layer for comprehensive prediction of disease risk. The novelty of our approach lies in our two-step framework: (i) utilizing the CNN’s effectiveness to extract the complex gene associations within individual genetic pathways and (ii) fusing features from ensemble of CNNs through Canonical Correlation Regularization layer to incorporate the interactions between pathways which share common genes. During prediction, we also address the important issues of interpretability of neural network models, and identifying the pathways and genes playing an important role in prediction.Implementation of our methodology into three real cancer genetic datasets for different prediction tasks validates our model’s generalizability and robustness. Comparing with conventional models, our methodology provides consistently better performance with AUC improvement of 11% on predicting early/late-stage kidney cancer, 10% on predicting kidney versus liver cancer type and 7% on predicting survival status in ovarian cancer as compared to the next best conventional machine learning model. The robust performance of our deep learning algorithm indicates that disease prediction using neural networks in multiple functionally related genes across different pathways improves genetic data-based prediction and understanding molecular mechanisms of diseases.https://github.com/divya031090/ReGeNNe. Accurate diagnosis and prognosis of diseases requires the clinicians to analyze patient biopsies, imaging or blood samples for characteristic disease biomarkers including unregulated pathways or genes .Recent studies have explored diagnosis prediction through disease–gene and disease–pathway associations using machine learning methodologies   to captuHowever, currently, as per our knowledge, there is no comprehensive deep learning methodology to efficiently assess disease prediction taking into consideration overlapping genes between pathways using a regularization approach. In this article, we propose an end-to-end framework for combining genetic data into pathway structure and using CCA along with neural network (NN) modeling for prediction of disease status and outcomes. A broad overview of our goal is illustrated in ReGeNNe as shown in To take advantage of end-to-end deep learning and prediction through genetic data while collaboratively benefiting from capturing the correlations between the gene pathways, we propose a framework for an ensemble-based NN modeling g genes in a pathway. Next, this matrix is vectorized with each row represented by a single coefficient, calculated with respect to all the genes as:In this step, firstly, the individual genes are mapped to their respective pathways and given as input to CNN models. CNNs are known to take advantage of local spatial coherence of input data in turn being able to dramatically reduce the number of operations needed to process the input due to convolution on patches of adjacent nodes using the local connectivity property. Making use of this property, within each pathway, genes are ordered based on their correlation with each other using Spearman correlation rank to make the adjacent genes in the input data similar in nature to accurately capture spatial structure during network learning. Such an ordering based on correlation/similarities between the genes is conducted to fulfill the requirement of the CNN for the input nodes to demonstrate spatial similarities  between the two sets is represented as:To utilize CCA in our CNN network, we start with a CNN composed of L layers. Denote its network parameters aslth network layer. In the setting of supervised learning, given M training samples i = 1 to M, the network parameters where where Loss(·) is a function such as cross-entropy loss. Adding regularization to the network training objective results in the following optimization problem:R(·) is the regularizer to be specified, and where lth network layer, For a specified S, applying CCA to the Therefore, if we have two pathway sets where I is an identity matrix of compatible size.s.t. Under the framework of regularized function learning, we train network parameters by penalizing objectives of the main learning tasks with CCA regularization such as:We then forward the output of the canonical regularization layer to the final fully connected layer which has a binary node with a softmax activation  transformed RSEM normalized count. Genes are mapped into the human genome coordinates using UCSC Xena HUGO probeMap (see ID/Gene mapping link below for details). All the three datasets comprised of 19 503 genes and 1387 pathways.Given the remarkable advances in high-throughput technologies, the development of The Cancer Genome Atlas (TCGA) (Kidney cancer dataset: The outcome space consisted of 945 subjects with 553 subjects with early-stage kidney cancer (Stage I and II) and 392 with late-stage kidney cancer (Stage III and IV). Our algorithm aimed at classifying early or late-stage kidney cancer based on their genetic and pathway information.Combined liver and kidney cancer dataset: In this dataset, we aimed at differentiating between the cancer type (liver/kidney) based on the genetic data. We combined the samples from the liver and kidney cancer dataset to create a combined cohort of subjects. In this dataset, we had a total of 1383 subjects with 945 subjects for kidney cancer and 438 for liver cancer.Ovarian cancer dataset: In this study, the outcome space comprised of 362 subjects with overall survival at 2 years. We split the patients into alive and deceased groups with respect to their survival status. Patients whose survival days were <2 years were removed from our subject population. 120 subjects were alive at 2 years and 242 were categorized as dead based on the 2-year survival status.ReGeNNe model and 30% as the test data to validate the model performance. An internal validation was done using 10 times 10-fold cross validation on the training set, to analyze model performance before testing and to eliminate overfitting. For each fold of the cross-validation, 90% of the total training set was selected at random for training, and the remaining 10% was selected as a holdout set for testing. We obtained 10 Area Under the Curve (AUC) values corresponding to initial 10-folds in the training set. We repeated this process 10 times in order to generate corresponding 100 AUC values. We then calculated the 95% confidence intervals (CIs) using these 100 AUC values. Our CNN framework is presented in For the NN modeling, the datasets were divided into 70% as the training data to train the Four-hundred epochs were run for the NN model with a stride size of 1, batch size of 5 and number of filters in the CNN network as 32 refer . Each nek pathways in the dataset, each pathway is leading to k-1 interactions with other pathways in the dataset as shown in For CCA, as shown in ReGeNNe model under the null is shown in For a fair comparison, we directly compared the ReGeNNe model which is essentially a stratified CNN with regularization with a stratified CNN without regularization to see the effectiveness of the CCA regularization layer. Further, we also compared the results obtained by our proposed model against conventional machine learning models which includes, Random Forests (RF) , Lasso rReGeNNe consistently performed better in terms of mean AUC and 95% CI as compared to other conventional ML approaches. Stratified CNN without considering interaction between pathways through regularization was next in performance to our methodology with mean AUC of 0.822, 0.775, and 0.81 on the kidney cancer, kidney versus liver cancer and ovarian cancer datasets respectively showing that stratification based on pathway structure aids in improving predictive ability as compared to other approaches. Incorporating the interaction between pathways through our approach further improves mean AUC to 0.853, 0.803, and 0.841 on the kidney cancer, kidney versus liver cancer, and ovarian cancer datasets.For the three datasets we evaluated the 10-fold cross validation performances on their training set as shown in Predicting early- and late-stage kidney cancer: We conducted the validation analyses on the test set of the Kidney cancer dataset . In the future, we would develop a scalable model taking into consideration all pathways to evaluate the predictive ability in a more comprehensive manner. Our current approach was also limited to a fixed outcome definition such as, case versus control, binary survival status, etc. at single timepoint. In the future, it would be interesting to analyze our approach on datasets with varying longitudinal outcomes at different time-points, and time to event outcomes. Adding environmental factors along with gene data as an input to the NN modeling also serves as an interesting area for future research. Lastly, although our implementation on three cancer datasets shows our model’s robustness, in the future, we would explore other disease datasets to compare the predictive performance.In conclusion, the strength of our methodology lies in efficient feature extraction from complex genetic pathway data through a CCA regularized CNN modeling. We believe pathway-based approaches that model joint effects of genetic variations in multiple functionally related genes across different pathways, are a major step forward in improving the performance of genetic data-based prediction and understanding molecular mechanisms of diseases. Despite the challenges associated with NN pathway-based analysis of genetic data, we estimate that the predictive ability and future potentials of these methods will increase as the coverage and quality of gene annotation databases improve.btad679_Supplementary_DataClick here for additional data file.


Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses. Next-generation sequencing (NGS) technologies have been rapidly advancing and are more widely used for clinical biomarker testing in recent years , 2. RNA The commonly used library preparation methods for RNA-seq, such as poly-A enrichment or ribosomal RNA (rRNA) depletion, are appropriate for fresh frozen (FF) samples with relatively high RNA integrity, while the application of rRNA depletion and other library preparation methods for sequencing of FFPE samples have also been used to generate acceptable transcriptomic data , 13–18. To identify the best method for RNA profiling of FFPE tissue, the current study evaluated the quality of gene expression profiles of a set of FFPE and case-matched FF tumor tissues from triple-negative breast cancer (TNBC) patients. FF tissue served as the gold standard reference as it does not have the same degradation issues as FFPE samples. The paired comparisons were made for each of the following RNA-seq library preparation protocols: RNAaccess (an exome-capture method), RiboZero (an rRNA depletion method) and Polyadenylation (a PolyA enrichment method)  patients. The new minimum quality and input defined in this study are lower than the cutoffs outlined in the Illumina guidelines  and woulTo identify the optimal RNA-seq library preparation protocol for FFPE samples, tissues from seven TNBC patients with FFPE and case-matched FF (See Methods for sample details) as well as two separate cases with FF normal breast tissues were used to generate RNA-seq data using three library preparation methods including PolyA, RiboZero and RNAaccess. The FF samples were processed by all three library preparation protocols: PolyA, RiboZero and RNAaccess, whereas FFPE samples were processed by RiboZero and RNAaccess . When comparing against the same FF.PolyA reference, FFPE.RNAaccess also had a better concordance than FFPE.RiboZero (R2 = 0.90 vs 0.75)  . The betIn addition, we explored well-validated signatures for breast cancer phenotype classifications and prognosis. A comparison between protocols resulted in similar concordance scores between FFPE and FF across the signatures except for Veridex, where RNAaccess performed much better than RiboZero. This was evident both when comparing within the same library preparation and when using PolyA on FF as the reference . A similOverall, these results show that both RiboZero and RNAaccess protocols can generate highly concordant coding gene expressions and signature scores between FF and FFPE samples, with RNAaccess producing equal or higher concordance scores.As clinical samples often have limited RNA yield and FFPE gastric cancer (GC) clinical samples tend to be highly degraded, we explored RNA-seq performance at various RNA input amounts and quality levels using a GC sample set to define the cutoffs for both metrics that would ideally allow for inclusion of a larger number of clinical samples in RNA-seq analyses. A GC FF/FFPE cohort was generated with an extensive range of DV200 scores (DV200 = 4–96%) as a measure of RNA integrity at various inputs including 10ng, 20ng, 50ng and 100ng  had the lowest correlation coefficients, with Pearson’s r values range from 0.564 to 0.733 when comparing 10ng, 20ng and 50ng to 100ng input levels  compared to 100ng, which occurred only in FFPE samples with DV200 below 10% . In thisFinally, NanoString nCounter technology has been suggested to offer advantages in profiling gene expression in FFPE samples with degraded RNA  possiblyThis study had the following limitations: 1) Only breast and gastric cancer tissues were included in our study, and we recognize that the results might be tissue-specific. 2) We have demonstrated higher concordance between FF and FFPE in terms of coding gene expression when using RNAaccess compared to RiboZero. However, RNAaccess is an exome-capture method, and if the purpose of a study is to quantify non-coding RNAs or to investigate the biology of precursor mRNA, RiboZero should be considered as the method of choice . 3) WhenWe identified RNAaccess as the preferred RNA-seq library preparation method for transcriptomic profiling of FFPE samples due to its consistent performance and the concordant gene expression profiles between FFPE and matched FF tissues. Furthermore, our study defined RNA quality of DV200 above 10% and input of at least 10ng of RNA as acceptable for sequencing of FFPE sample using RNA-seq RNAaccess, which is likely to increase the number of clinical FFPE samples that can be tested. In addition, the current study demonstrated that FFPE expression profiles of shared genes using RNAaccess were comparable to those of NanoString. However, RNAaccess may be a preferred platform for exploratory and discovery oncology research due to the vastly higher genome-wide coverage.All human tissue samples were purchased from commercial vendors. All sample collections were conducted by vendors under IRB-approved protocols, and all donors signed informed consent forms. Samples were collected from 2009 to 2015. The study was conducted from 2015 to 2018. Authors had no access to information that could identify individual participants during or after the study.Triple-negative breast cancer/breast tissue set (TNBC): Seven cases with paired fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) triple negative breast cancer samples were purchased from a commercial vendor. One FFPE tissue was excluded from analysis due to sequencing failure. Additionally, two FF cases of commercially procured normal breast tissue were included in the sample set (Panels A-C in Gastric cancer/stomach tissue set (GC): Tumor tissue samples were purchased from commercial vendors. FFPE blocks were obtained from 12 GC patients . Of thesRNA in all experiments in this study was extracted using miRNeasy kit (Qiagen). For the TNBC dataset, RNA-Seq libraries from each FF sample were prepared using three methods: the Illumina TruSeq Ribo-Zero kit with Ribo-Zero Gold (RiboZero), Illumina TruSeq RNA access Library Prep Kit  and Illumina TruSeq Stranded mRNA kit with oligo-dT beads capturing polyA tails (PolyA) from 100ng input of total RNA see . All libThe quality assessment of raw sequencing data was performed using FastQC (v0.10.1) . After qThe NanoString nCounter PanCancer Immune Profiling Panel , using 7For comparison between library preparation and tissue preservation protocols in terms of gene expression, Pearson’s r was calculated using log2CPM values of protein-coding genes only. For the expression correlation by quantiles in TNBC, genes from the reference sample were grouped into 10 windows ranging from high to low expression. The correlation coefficient for the particular comparison was then calculated using genes in each window for each subject. Loess regression was fit on the computed correlation coefficients to generate the overall estimate with confidence.Across the 11 gastric cancer subjects with different RNA inputs for FFPE RNAaccess, Pearson’s r values were plotted as box and whisker plots comparing each of 3 RNA input levels  to the gold standard of 100ng for RNAaccess. Results were stratified by each of the following DV200 RNA degradation categories: slightly degraded , very degraded  and severely degraded . Coefficients of variation (CV) were calculated in log space to assess gene-level variability for each RNA dilution comparison by DV200 category. Additionally, given that standard deviation (SD) could be a more appropriate metric for measuring variability for log-transformed gene counts, the gene-level variability was also assessed using SD, which yielded similar results. Hence only CV results are shown in the current study.TNBC samples were subtyped by the PAM50 signature , which c®  [hypoxic or non-hypoxic tumors; and 21-gene-recurrence-score (RS) or Oncotype DX®  [Additional signatures for breast cancer phenotyping and prognostics include 70-gene profile or MammaPrinterlands) –49 for perlands) –52 for perlands) , 54 for ity, CA)  for predity, CA) . The genity, CA) . See S1 In the GC dataset, paired RNAaccess and NanoString samples were available for five overlapped subjects. We investigated T-cell inflammation by measuring pathway gene expression of an IFNγ signature known to be predictive of response to PD-L1 inhibition in gastric cancer patients  as well Unless specified otherwise, all analyses were carried out in R statistical environment version 3.2.3 or newer. The statistical tests in this study include Wilcoxon rank sum test, paired t-test and Pearson correlation (simple linear regression).S1 Fig(A) Experimental design using breast tissues for FF and FFPE concordance by library preparation methods. (B-C) Upset plots for relationships between samples and subjects in the breast tissue cohort. (D) Experimental design using gastric cancer tissues for RNA quality and input optimization in RNAaccess as well as for comparing RNAaccess with NanoString. (E) Upset plots for relationships between samples and subjects in the gastric cancer tissue cohort. N in each design schematic represents the number of subjects. The top barplot in each upset plot panel summarizes subject size, whereas the left barplot summarizes sample size.(PDF)Click here for additional data file.S2 Fig(A-C) Total reads mapping rates and rRNA rate in different library preparation and tissue preservation methods. Blue lines connect samples from the same subjects. P-values are based on Wilcoxon rank sum test.(PDF)Click here for additional data file.S3 Fig(A) Correlations of all protein-coding genes between protocols for each subject. Pearson’s r was used for assessing the correlations. (B) Cumulative distribution of global coding gene expression for each library preservation and preparation method. CPM: count per million reads. Note that FFPE.RNAaccess and FFPE.RiboZero were compared to FF processed by the third library preparation method (PolyA) to supplement the within-protocol FFPE to FF comparison in (PDF)Click here for additional data file.S4 Fig(A) Pearson correlation between RNAaccess and RiboZero for FF samples by expression quantiles. Grey curves and the shades were derived using loess regression and can be regarded as the average correlation across subjects. Blue and green dotted lines were added for better visualizing the difference between comparisons. Note that the average curve is not above r = 0.15 for the majority of quantiles, which is lower than within-library-preparation-kit correlation between FFPE and FF in (PDF)Click here for additional data file.S5 Fig(A) correlation on gene expression within each signature between FFPE and FF for RiboZero or RNAaccess. P-values were calculated based on Wilcoxon one-tailed signed rank test. (B) mean and variance of the signature values across subjects for each protocol. Veridex has scores with low average but high variance, and the FFPE.RiboZero is relatively far from other protocols.(PDF)Click here for additional data file.S6 Fig(A) Correlation between exonic mapping rate and DV200 and RNA input levels. Note that outlier samples are from a different batch (B) Correlation between percent multi-mapped reads and DV200 and RNA input levels.(PDF)Click here for additional data file.S7 Fig(A-B) Within-subject correlation between RNA input levels for two GC subjects with lowest (A) and highest (B) DV200. In the subject with the lowest DV200 score of 4, there is a greater proportion of low expressing genes as RNA input level decreases for correlations with 100ng. In contrast, the subject with the highest DV200 score of 34 had far more consistent data at all RNA input levels.(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Since cancer cell-derived exosomes are often mixed with those from healthy cells in liquid biopsy of tumour patients, accurately measuring the purity of tumour cell-derived exosomes is not only critical for the early detection but also essential for unbiased identification of diagnosis biomarkers. Here, we propose ‘ExosomePurity’, a tumour purity deconvolution model to estimate tumour purity in serum exosomes of cancer patients based on microribonucleic acid (miRNA)-Seq data. We first identify the differently expressed miRNAs as signature to distinguish cancer cell- from healthy cell-derived exosomes. Then, the deconvolution model was developed to estimate the proportions of cancer exosomes and normal exosomes in serum. The purity predicted by the model shows high correlation with actual purity in simulated data and actual data. Moreover, the model is robust under the different levels of noise background. The tumour purity was also used to correct differential expressed gene analysis. ExosomePurity empowers the research community to study non-invasive early diagnosis and to track cancer progression in cancers more efficiently. It is implemented in R and is freely available from GitHub ( Cancer is a genetic disease in which tumour cells grow uncontrolled and invade nearby tissues or spread to other parts of the body . As a leExosomes are a class of extracellular vesicles, which are derived from cells through exocytosis and ingested by target cells, transferring biological signals to local or distant cells , 6. ExosIt is noteworthy that liquid biopsy of tumour patients contains the mixed sources of exosomes including secreted from cancer cells and healthy cells . IdentifThe deconvolution method is a common technique in signal and image processing. In these fields, deconvolution is used to reverse the effects of convolution, which is the mathematical operation that occurs when a signal or image is passed through a system that modifies it. By applying deconvolution, one can attempt to recover the original signal or image before it was convolved. In recent years, the deconvolution method has been applied in biology to estimate cellular composition from the methylation data , 26, bulHere we propose ‘ExosomePurity’, a tumour exosome purity deconvolution model to estimate tumour sourced exosome purity in serum exosomes of cancer patients based on miRNA signatures. Firstly, we interrogated miRNA-Seq data to identify the differently expressed miRNAs as miRNA signatures to distinguish cancer cell- from healthy cell-derived exosomes. The generalization of the signatures was evaluated in the independent data. Then the deconvolution model was developed to estimate the tumour exosome purity in serum exosomes of cancer patients. The performance and robustness of this purity model were evaluated on actual and simulated data. Finally, we used the tumour exosome purity to correct differential expressed gene (DEG) analysis.We developed ‘ExosomePurity’, a tumour purity deconvolution model to estimate the tumour exosome purity in serum exosomes of cancer patients . Firstlyocument} . T repre Θ and Φ –D. The mWe assumed that the serum exosomes of tumour patients are sourced from cancer cell- and healthy cell-derived exosomes, whose miRNA expression patterns can be speculated from cancer cell line exosomes and healthy controls. Here we first generated an miRNA signature profile to depict the expression patterns of miRNAs from cancer cell- and healthy cell-derived exosomes. An miRNA signature profile is expected to use for accurately distinguishing cancer exosomes from healthy controls. We selected the miRNAs that are differentially expressed between cancer cell line exosomes and healthy controls measured by DESeq2 (version 1.30.1)  and stabIn an miRNA signature profile, for any miRNA mentclass2pt{minimFor miRNA Equation ). An arrquations  and 5)))\documenSolve.QP function in the quadprog package in R to solve the matrix.For each sample Equation ). We use1)Simulated exosome miRNA-Seq dataWe generated simulated miRNA-Seq data of known tumour purity by combining the actual data Θ from cancer cell line exosomes and healthy controls. Simulated tumour exosome data with purity x% were designed by x% expression profile of cancer cell line and (100-x)% of healthy controls. Two purity ranges of datasets were simulated: (1) from 0 to 1 and (2) from 0 to 0.1.2) Actual exosome miRNA-Seq datahttps://www.ncbi.nlm.nih.gov/geo/) [The actual exosome miRNA-Seq data data Θ,  were obtov/geo/) . These dTo investigate if miRNAs in the model are tumour specific, we conducted an analysis using three publicly available databases: dbMEMC , CancerMhttps://cutadapt.readthedocs.io/en/stable/). The filtered reads were aligned to the human genome using bowtie (version 1.2.1) with options ‘-n 1 -l 16 -p 7 -a --best –strata’ [counts per million for miRNA. For differential gene expression analysis, raw reads for miRNAs quantified by featureCounts (version 1.5.3) were then analyzed by the Bioconductor package DESeq2 (version 1.30.1) [Raw reads of the miRNA-Seq data were processed by removing low-quality reads, adaptor dimers and sequences with lengths < 18 and > 35 nucleotides using cutadapt (version 2.3)  .0) left as an independent cohort and the remaining four parts (Θ1) used for 3-fold cross-validation. Additionally, we used external Φ as another independent cohort. More specifically, for simulated data evaluation, we applied two-thirds of Θ1 to generate the signatures and the remaining one-third and Θ0 to produce simulated data with the varied purities for validation. The purities ranged from 0 to 1 and from 0 to 0.1. The purity 0–0.1 was designed to evaluate if the model works for the early diagnosis of tumour. The Pearson correlation (PC) as a performance benchmarking was applied to evaluate the consistency of the simulated tumour purity and the predicted tumour purity. For actual data evaluation, we applied Θ1 for 3-fold cross-validation, and Θ0 and Φ as independent cohorts.We divided the samples in Θ into five parts, with one of them (Θ0) of tumour purities, and tested whether the tumour purity (tPurity) for a given sample was from H0. If p(tPurity| H0) < 0.05, we rejected H0 and considered the sample as cancer cell-derived exosomes.To accurately differentiate between tumour and normal samples, we used healthy cell-derived exosomes to generate null distribution .For miRNA quations ).For Equation ). For gimentclasspt{minimaP-value. Benjamini–Hochberg’s method is applied on P-values to obtain FDRs. The model parameters can be solved by the generalized least square method [For the hypothesis test e method .In addition, the analysis of uncorrected differentially expressed miRNAs was performed using the Deseq2 packages (version 1.30.1). Differentially expressed miRNAs with FDR values <0.05 and |log2FC| >1 were considered to be significant. Differentially expressed miRNAs were analyzed by DIANA-miRPath (version 3.0) to identify their targets and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signalling pathways . The sigThe Wilcoxon rank-sum test was used to compare purity. The PC and mean absolute errors (MAE) between predicted and simulated purity were applied to evaluate the performance of the purity model. All statistical analyses were executed in R (version 4.0.3).To identify miRNA signatures in cancer exosomes, we interrogated miRNA-Seq data Θ and applied Deseq2 to evaluate expression levels across samples. The differentially expressed miRNAs in cancer exosomes were further narrowed down in terms of their expression stability measured by variances with their values less than 2 both in cancer cell- and healthy cell-derived exosomes. Our analysis led to the identification of miRNA signatures in cancer exosomes of 11 cancer types . EspeciaTo evaluate the robustness of miRNA signatures, we applied re-sampling technology with the two-thirds of samples in datasets Θ to make the signatures and observed the consistency of the signatures when different samples were employed. The results showed that the signatures from three-time sampling were consistent with those from all samples . SpecialOur analysis identified 548 miRNAs that are either up- or down-regulated in individual cancers . We furt1 to generate the signatures and the remaining one-third and Θ0 to produce simulated data for validation. The varied purities of the simulated data range from 0 to 1 and from 0 to 0.1 . The PC was applied to evaluate the consistency of the simulated tumour purity and the predicted tumour purity. The results showed that when the tumour purity ranges from 0 to 1, PCs were 1 in breast cancer, 0.99 in lung cancer and 0.99 in colorectal cancer, showing that the purity model was robust for 3-fold cross-validation  left as an independent cohort and the remaining four parts (Θ1) used for 3-fold cross-validation. Additionally, we used external Φ as another independent cohort.We further evaluated the model on two types of actual data: (i) miRNA-Seq datasets Θ and (ii) an external cohort Φ. We divided the samples in Θ into five parts, with one of them . Besides, a similar result was observed between the patients with lung cancer and lung granuloma .Furthermore, miRNA signatures were generalized to an external cohort Φ, including exosome datasets from the patients with nine cancer types and healthy controls. The results showed that the purity of different cancer samples varied considerably and healthy controls were close to 0 absolutely . In part0) of tumour purities, and tested whether cancer cell line-derived exosomes in datasets Θ, patients’ and healthy individuals’ exosomes in datasets Φ can be accurately predicted to be from tumour or normal samples. We further used Precision, Recall, Specificity and F1—a combined measure of Precision and Recall—as performance benchmarks to evaluate the model. The results demonstrated our model achieved high performance with zero false negatives and acceptable false positives  using TCGA patient data. Notably, 41 (89%) were reported to be up- or down-regulated in at least one database . Next, TOur analysis showed that tumour purity varied among tumour exosomes, moreover, about 10%–40% of which were from healthy cell-derived ones . This wiTumour-derived exosomes can be harnessed as non-invasive diagnosis and prognostic biomarkers because they are enriched in biological fluids and carry tumour-characterized biomolecules . ExosomeTherefore, we propose the R-based tumour purity deconvolution model ‘ExosomePurity’ to address this unmet need and enable researchers to accurately estimate tumour exosome purity from miRNA-Seq data in serum exosomes of cancer patients. Our study currently used this model in 11 cancer types. Utmost, it can be extended to any cancers, provided sufficient serum exosome sequencing data. The purity model was evaluated by actual and simulated data with purity ranging from 0 to 1 and from 0 to 0.1 as the application for early diagnosis. The median predicted purity is close to 1 in actual cancer cell-derived exosomes and close to 0 in actual healthy cell-derived exosomes. The purity predicted by the model shows high correlation with simulated purity in simulated data . In addiIn recent years, numerous studies have shown that miRNAs can be circulated in biological fluids and serve as the biomarkers for diagnosis and prognosis. For example, hsa-miR-21 is involved in glioblastoma development and can predict tumour recurrence or metastasis . MoreoveTumour purity is an important measurement for tumour samples, reflecting cancer progression, tumour microenvironment, the perturbed pathways et al . Our stuIn summary, we developed ExosomePurity, a tumour exosome purity deconvolution model to estimate tumour sourced exosome purity in serum exosomes of cancer patients based on miRNA signatures. ExosomePurity empowers the research community to study non-invasive early diagnosis and track cancer progression in cancers more efficiently.We propose ‘ExosomePurity’, a tumour purity deconvolution model to estimate tumour purity in serum exosomes of cancer patients based on miRNA signatures.The deconvolution models of individual cancers and pan-cancer are developed and gain the excellent performance in simulated and actual data of 11 individual cancers and pan-cancer.ExosomePurity generates miRNA signatures of individual cancers and pan-cancer, which achieve the good prediction ability for tumour purity and clinical outcome, deserving further investigation on their regulatory mechanisms during tumorigenesis and development.ExosomePurity empowers the research community to study non-invasive early diagnosis and track cancer progression in cancers efficiently.Supplementary_Figures_bbad119Click here for additional data file.Table_S1_S2_S3_bbad119Click here for additional data file.