SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes ( HCC is ranked as the third most common malignancy globally and is highly prevalent in Asia including in China . ApproxiClinical treatment methods of liver cancer are surgical resection, chemotherapy, molecular targeted therapy, and liver transplantation have made encouraging progress in the past few decades . HoweverCompeting endogenous RNAs (ceRNA) are transcripts that regulate target genes at the posttranscriptional level by competitively binding to the same small RNA . miRNAs The cancer genome atlas (TCGA), which is an open-access database containing genome-wide sequencing data sets for 33 cancers and more than 10,000 tumor samples , is wide22.1https://gdc.cancer.gov/developers/gdc-application-programming-interface-api) on 24 January 2019. The RNA-seq data were composed of 371 liver cancer tissues and 50 adjacent tissue samples. We downloaded Count and fragments per kilobase of transcript per million fragments mapped (FPKM), and Li et al.’s method [https://www.gencodegenes.org/human/). Moreover, international cancer genome consortium-Japan (ICGC-JP) data were obtained from the hepatocellular carcinoma database (HCCDB database (http://lifeome.net/database/hccdb/home.html), and this contained RNA-seq (including lncRNA and mRNA) and miRNA sequencing (miRNA-seq) data sets of 212 samples, and the latest clinical follow-up information. The workflow is shown in The RNA-seq expression (including lncRNAs and mRNAs), miRNA sequencing (miRNA-seq) data sets, and the latest clinical follow-up data were downloaded from the TCGA genomic data commons (s method  was furt2.2http://starbase.sysu.edu.cn/) integrates large-scale CLIP-seq  data to decode interaction network. The miRNA target genes were predicted by five types of prediction algorithms, namely, TargetScan (http://www.targetscan.org/vert_72/), miRanda (http://miranda.org.uk/), Pictar (https://pictar.mdc-berlin.de/), PITA, and RNA22 (https://omictools.com/rna22-tool). The 1,055,319 miRNA–mRNA interaction data containing 484 miRNAs, 15,064 mRNAs, and 63,698 miRNA–lncRNA interaction data  were downloaded from starBase ev3.0. We introduced a hypergeometric model [StarBase V3.0 . The clinical characteristics of the three data sets are shown in p <0.01). The least absolute shrinkage and selection operator (Lasso) Tibshirani (1996) method was used to further reduce the gene range. Thereafter, a prognostic model based on these genes was established. The R software package glmnet [In this experiment, we obtained the TCGA dataset and selected samples with a follow-up time of longer than 30 days. The TCGA data were grouped at 1:1 ratio. The training set and TCGA test set contained 170 and 169 samples, respectively. The ICGC data set served as an external validation set (e glmnet  (Divisio2.5Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted in the R package clusterProfiler  to ident2.6t-test and two-sided tests were used for statistical tests. Pearson correlation coefficient was applied for correlation analysis. Cytoscape [http://www.cytoscape.org/) was conducted for network visualization. The Benjamini–Hochberg method was used to convert the p-value to FDR. Survival curves for each subgroup in the data set was plotted by The Kaplan–Meier method. Additionally, the log-rank test was used to determine statistically significant differences at p <0.05.All analyses were performed in R packages 3.4.3 using default parameters unless otherwise stated. Student’s ytoscape   while those with AFP > 300 showed no significant margins, which may be explained by limited sample size. Relevant HR, 95% CI of HR were analyzed, and the p-value using univariate and multivariate Cox regression on the TCGA and ICGC data to determine whether the 4-mRNA signature model can be independently used in clinical applications. Clinical information on TCGA and ICGC patient records including age, gender, pathology T phase, node phase, metastasis phase, tumor stage, and our 4-mRNA signature grouping information were systematically analyzed (p = 1.47 × 10−5), and M stage were significantly associated with survival. In the ICGC dataset, univariate Cox regression analysis revealed that the risk score and Stage III/IV vs Stage I/II were significantly associated with survival. Also, from the data of the corresponding multivariate Cox regression analysis, it was found that the risk score , gender, and Stage III/IV vs Stage I/II were significantly related to HCC survival. The above results indicated that our 4-mRNA signature model had a high performance in clinical prediction of HCC survival.From the results of survival analysis, only the tumor-node-metastasis (TNM) and tumor phases were found to be significantly correlated with the OS of HCC in the TCGA training set . A subgroup analysis of the four-gene signature revealed that 4-mRNA signature could significantly distinguish young, old, AFP ≤300, male, white, Asian, Stage I + II, grade I +, and grade III patients from high-risk and low-risk groups . Patientanalyzed . In the 3.6The correlation of the risk score in different samples with the biological functions was determined. We obtained the single-sample GSEA score corresponding to each sample through using different function calculations, and the correlation of these functions with the risk score was also analyzed. Function with a correlation value greater than 0.5 and FDR <0.01 was filtered . NotablyFour prognostic risk models including six-gene signature  , eight-g4SAPCD2, DKC1, CHRNA5, and UROD) related to the OS of HCC. Furthermore, the signature was verified as an independent indicator using internal data sets and external validation queues. Functional analysis showed that risk score values were correlated with cell cycle, DNA replication, glycerolipid metabolism, and histidine metabolism.In this study, we obtained lncRNA, mRNA, and miRNA expression profiles from the TCGA database and developed a ceRNA network for HCC, based on which 53 differentially expressed mRNAs were identified to be able to independently predict OS of HCC patients. Notably, comprehensive analysis on 53 mRNAs using LASSO regression generated four a gene-based signature  . However, previous studies have reported the prognostic significance of DEGs in cancer and their dysregulation in tumor tissues. Moreover, studies reported that DKC1 upregulation is frequently observed in many different human cancers including in HCC, and HCC tissues show a positive association with nuclear DKC1 levels . Additiodiseases . A previdiseases . CHRNA5 diseases ,42,43. HDespite the novel findings, here we reported some limitations of this study. First, the samples lacked clinical follow-up information; therefore, factors such as other health statuses of patients were not included in distinguishing clinical outcomes. Second, the results obtained by bioinformatics analysis may not be convincing enough and require experimental verification. Therefore, further genetic and experimental studies incorporating larger sample sizes and experimental validation are needed.This study identified several differentially expressed genes, miRNAs, and lncRNAs from primary HCC tumors and adjacent normal liver tissues. A ceRNA network for HCC was established based on the maladjusted RNAs, and a prognostic signature for predicting the OS of HCC patients was developed. The current findings highlighted the underlying mechanism through which dysregulated RNAs participate in the development and prognosis of HCC. The results of this study provide new insights into the development of novel clinical diagnostic and therapeutic biomarkers for HCC.


Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets. Single-cell expression data, such as from cytometry and single-cell RNA-seq (scRNA-seq), provide information on cell population profiles. The greatest benefit of single-cell expression data is that it can reveal the heterogeneity of cell populations. Since scRNA-seq datasets contain many cells and many genes, computational methods to conduct dimensionality reduction in a data-driven manner such as PCA, tSNE, or UMAP are indispensable for their data analysis . In suchOn the other hand, comparing cell population profiles between case and control donors provides information on the mechanism of complex diseases. Comparison of single-cell expression datasets from multiple donors requires data mining of datasets with donors, cells, and genes. This type of analysis is thus more complex than donor comparisons for bulk expression datasets with only donors and genes. When the purpose is comparing multiple donor profiles, the donors should be embedded into a low-dimensional coordinate space to be compared to each other  into a matrix format in a completely data-driven manner without any biological assumptions.We have developed a completely data-driven method, named kernelDEEF, to convert multiple high-dimensional single-cell expression profiles into a donor-by-feature matrix format in a completely data-driven manner , and the right panel is the CD4+ T cell and CD8+ T cell fractions. In a cellular subset-based comparison, a multi-step gating process is applied, and only a portion of the information in the cellular population profile is used. kernelDEEF provides a consistent workflow for feature extraction, and the differences between patient groups are identified on θ coordinate plots. Figure θ1 and θ2 coordinate values, which suggested that the result is quite stable for the resampling .We investigated the stability of our completely data-driven approach. We performed another two replications by cell resampling n = 1000) and drew a coplot of 000 and dθ coordinate plots of scRNA-seq data of PBMCs from patients with ulcerative colitis (UC), a representative inflammatory bowel disease, and control donors  is a matrix of a donor-by-θ coordinate values where the subscripts indicate the order of the contributions expressed in the eigenvalues from the largest positive (θ1) to the largest negative value. As a result, the information in the dataset is converted from a donor to a feature matrix.In this method, single-cell expression data can be thought of as a statistical sample from a high-dimensional probability distribution. The first step is to calculate the inner-product matrix between the donors. For the inner-product of the distributions, we use an index based on kernel mean embedding . We pickument}1p . Next, wument}1p . This meWe used a publicly available cytometry dataset (ITN) as an example of cytometry implementation. The ITN dataset is a built-in cytometry dataset of the R package flowStats , 8. The We performed automatic cellular subset fraction quantification with the code described in the vignette document of the flowStats package. Here, CD4+ and CD8+ T cells were identified by sequential application of CD3/SSC lymphocyte gating and CD4/CD8 gating.All raw profiles in the ITN dataset were transformed by asinh(intensity/5) for downstream analysis as a complete data-driven approach.n = 1000). For a comparison with kernelDEEF, we also performed a grid-wise inner-product calculation procedure using the ITN dataset [α and 1 − α percentile for each marker (α = 0.15). The range of expression values for this marker was divided into a grid of 10. Since the number of markers is five, the total number of grids is 105. For these grids, a density estimation was performed using the k nearest neighbor method (k = 100). The obtained densities were normalized to sum to 1, and the inner-products were calculated by vector calculation. The coordinates were obtained by applying DEEF to the inner-product matrix obtained in this way.We applied kernelDEEF to this dataset  . We usedWe used three datasets  from a previous study . HVTN, Hθ coordinates matrix, where the number of picked cells (n) was 1000. Only the coordinate axes corresponding to positive eigenvalues were used in subsequent analyses. These matrix data were used for the subsequent machine learning procedure. In the HVTN data implementation, the training data and test data were described in the original dataset, then we used the same split (the proportion of training data was 50%). Because the training and test labels were not described in AML and HEU vs. UE datasets, the datasets were randomly split to have the same training rate (50%) as in the original paper.We applied the kernelDEEF procedure and calculated donors by the θ coordinates to use were determined with the GridSerchCV function of scikit-learn, where the fold-number of cross-validation was set to five. To reduce the calculation burden of the grid search for how many top θ coordinates to use, the number of θ coordinates to be used was increased in order starting from 1, and if the best score of the cross-validation was not updated more than 11 times, the search was stopped. We conducted different cell resamplings and ensembles. After repeating the above prediction 25 times, the final predictive label was determined by a majority vote of these predictions. For AML and HEU vs. UE, after determining the optimal number of coordinates for each condition, XGBoost was applied to the integrated matrix created by concatenating θ coordinate matrices of all conditions in the columns.We used the XGBoost algorithm for sample classification with the Python library xgboost, where n_estimators = 5000 . The hypθ coordinates for the downstream analysis. For each gene, we calculated the Wilks’ lambda using the Python statsmodels library’s multivariate.manova.MANOVA function [We used the top two function . In addifunction .We sampled 672 cells from each donor and pooled them into UC and control groups, respectively. As such, the UC group contained 4704 (672 × 7) cells, and the control group contained 5376 (672 × 8) cells. scDD was applied using the R package to the expression data of these two groups with default settings.Additional File 1. Coplot of coordinate values for θ1 and θ2 in three cell resamplings  with the ITN dataset.Additional File 2. Top two θ coordinate plots of the ITN dataset with grid-wise estimated inner products and DEEF applied.Additional File 3. Performance in the HEU vs. UE dataset.Additional File 4. Performance in the AML dataset.Additional File 5. Coplot of Wiks’ lambda values of the genes for the three resamplings  in the UC dataset.Additional File 6. QQ plot to compare the distribution of -log10 calculated from scDD  with the null distribution obtained from the label permutation .Additional File 7. Coplot to compare -log10(lambda) calculated from our method (x axis) and -log10 calculated from scDD (y axis).


PRECISION.seq for assessing the performance of depth normalization in microRNA sequencing data. It provides a pair of microRNA sequencing data sets for the same set of tumor samples, additional pairs of data sets simulated by re-sampling under various patterns of differential expression, and a collection of numerical and graphical tools for assessing the performance of normalization methods. Users can easily assess their chosen normalization method and compare its performance to nine methods already included in the package. PRECISION.seq enables an objective and systematic evaluation of normalization methods in microRNA sequencing using realistically distributed and robustly benchmarked data under a wide range of differential expression patterns. To our best knowledge, this is the first such tool available. The data sets and source code of the R package can be found at https://github.com/LXQin/PRECISION.seq.We present a new R package  Depth normalization is a critical preprocessing step for accurate and reproducible analysis of transcriptomic sequencing data . MethodsPRECISION.seq, PaiREd miCrorna analysIs of differential expresSION for sequencing, for interested researchers to assess methods.To enable such an assessment, we collected two data sets for the same set of tumor samples, where one set was collected using uniform handling and balanced library assignment and the second was collected over time and without such careful study design . The fordata.benchmark and the second by data.test.MiRNAs were sequenced for 27 myxofibrosarcoma samples and 27 pleomorphic malignant fibrous histiocytoma samples twice, once with uniform handling (serving as the “benchmark” data) and a second time in the order of sample collection over the years resulting in unwanted depth variations (serving as the “test” data) . The firprecision.seq following three steps: first, the test data is normalized using one or multiple methods; second, differential expression between the two subtypes is determined in the un-normalized benchmark data and normalized test data using either voom-limma or edgeR , DESeq, PoissonSeq ; three mp-values at a user-specified significance level, the pip.statistics function calculates the True Positive Rate (TPR), False Positive Rate (FPR), False Discovery Rate (FDR), and False Negative Rate (FNR). To assess the impact of each individual normalization method, functions are included to draw 1) Relative Log Expression (RLE) plot for log2 count data (fig.RLE), 2) Volcano plot for p-values versus group mean differences (fig.volcano), and 3) Venn diagram of DE statuses (fig.venn) (fig.FDR_FNR), 2) Concordance At the Top (CAT) plot of the p-value ranking (fig.CAT), and 3) Dendrogram for hierarchically clustering p-values using the Euclidean distance and the Ward’s minimum variance linkage (fig.dendrogram)  . To compogram) .simulation.algorithm function that implements the re-sampling-based algorithm introduced in . We assess the performance of P90 in comparison with the nine aforementioned methods using the function ata sets . As expePRECISION.seq, for assessing the performance of depth normalization methods in miRNA sequencing using realistically distributed and robustly benchmarked data under a range of differential expression scenarios. To the best of our knowledge, this is the first such tool available. One limitation of our tool is that it does not offer varied scenarios for the number of samples or the range of sequencing depth in the samples.In this paper, we introduce an R package, called


Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14589006 This has been especially true in the cancer research field, which has leveraged a growing amount of available data from repositories such as the Gene Expression Omnibus (GEO)1, ArrayExpress2, and cBioPortal3 to facilitate pre-clinical modelling, delineate novel pathways involved in tumorigenesis, and discover clinically-relevant biomarkers. When such data is used in conjunction with third-party software such as Gene Set Enrichment Analysis (GSEA)4 and Ingenuity Pathway Analysis (IPA)5, transcriptome-wide analysis can provide a powerful tool for generating and testing hypotheses. However, because analytic performance depends on the quality and quantity of tissue samples, a plurality of investigations preferentially utilize datasets supplied by The Cancer Genome Atlas (TCGA) as they have been extensively validated and are robust in terms of sample number and included clinicopathology meta-data. Despite this, many TCGA datasets remain unsuitable for more specialized areas of cancer research. For example, tracking neoplasm development and progression using in silico approaches is constrained due to the lack of pre-malignant sample representation within TCGA datasets, a fact that is especially apparent for colorectal cancer (CRC)-related research6.Throughout the past decade, bioinformatics-based analyses have become a popular means for testing 7 and are readily testable in vitro and in vivo8. Although much focus has been directed towards dissecting mechanisms related to genomic alterations, a paradigm shift has occurred in the form of transcriptional profiling for elucidating key drivers and suppressors of early tumorigenesis. The relative ease of tissue acquisition combined with the development of more reliable and cost-effective array platforms has contributed to the considerable rise in the number of publicly available datasets containing adenoma samples. However, this rise has resulted in the publication of many discordant results for essentially the same underlying biological process, thereby posing major challenges for establishing consensus9. Because inter-study heterogeneity caused by differences in study design, sample preparation, patient cohorts, and choice of array platform (amongst others) further complicates this process, using conventional meta-analytic techniques such as random-effects modelling to generate consensus has proven insufficient as results are limited to gene-level summaries10. In contrast, merging pre-processed datasets followed by batch correction and gene filtering effectively enables more complex meta-transcriptomic analyses, even demonstrating results comparable in robustness to TCGA datasets in terms of included clinicopathologic meta-data12.CRC serves as an exemplary model for investigating neoplastic progression as molecular events contributing to adenoma formation and progression are well describedin silico modelling and bioinformatic analyses of colorectal neoplastic progression. The overall study design including our pipeline and technical validation is outlined in Fig. 13 to enable Meta-dataset construction without the use of the inSilicoDB R package. Briefly, microarray studies of the same chip platform (GPL570) and annotation package (hgu133plus2) meeting our inclusion criteria were identified using the Search Tag Analysis Resource for GEO (STARGEO) as a search proxy for GEO14. Pre-processing included downloading raw data from GEO followed by background correction, expression normalization, and log2 transformation via frozen Robust Multiarray Averaging (fRMA)15. The Meta-dataset was then generated by merging all pre-processed datasets by matching probe sets and then batch corrected using the empirical Bayes estimation method, or ComBat9. Thereafter, low variant probes were filtered from the Meta-dataset to facilitate technical validation and downstream differential expression analysis. We implemented two metrics to validate the reliability, accuracy, and robustness of the Meta-dataset. First, quantitative validation was performed via cross-platform correlation of gene trends with the TCGA colon adenocarcinoma (COAD) dataset6 and five external GEO datasets for all pairwise comparisons corresponding to normal versus adenoma (AvN), CRC versus adenoma (CvA), and CRC versus normal (CvN) signatures. Differentially expressed genes were then compared to four additional studies to assess overlap and consistency. Second, all signatures were functionally validated through integrative use of functional enrichment analyses using gene ontology (GO), gene set variance analysis (GSVA), and IPA with results being compared internally and to the literature.Here, we developed a merged Meta-dataset containing 231 normal, 132 adenoma, and 342 colon cancer tissue samples across twelve independent studies to serve as a central compendium for Ultimately, our Meta-dataset represents a centralized dataset for studying early colorectal neoplasm dynamics with a straightforward workflow that is readily adaptable for other types of cancer.14, to efficiently parse sample characteristics to identify studies meeting these criteria. Overall, twelve independent studies were chosen out of an initial list of 256 to be used in the construction of the Meta-dataset -compliant microarray studies sourced from GEO were selected based on predefined inclusion/exclusion criteria including: A) having either freshly prepared or frozen human tissue samples, B) a minimal number of four samples per tissue type, C) use of the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 array) for matching probe sets, and D) includes two of either normal, adenoma, or CRC tissue samples  R package16. Individual .CEL files for each study were unpacked using the untar function in base R, loaded using the ReadAffy function part of the affy (version 1.68.0) package17, and subsequently background corrected and log-transformed by frozen Robust Multiarray Averaging (fRMA) using the frma (version 1.42.0) package15. Compared to traditional RMA, fRMA utilizes pre-computed probe variances to normalize raw microarray data and was shown to outperform RMA when pre-processing individual datasets for grouped analyses15.Raw data contained in zipped .TAR packages were downloaded from GEO using the umap (version 0.2.7.0) package18 and removed using the original parametric iteration of ComBat within the sva (version 3.38.0) package19. UMAP was used over traditional principal component analysis (PCA) to identify batches due to its ability to better represent local relationships whilst preserving global structure, thereby accentuating non-biological batch clusters for rapid identification and confirmation of their removal post-processing18. For batch correction, ComBat was chosen due to its flexibility, reliability, and ability to set covariates of interest. Because ComBat assumes that differences in batches are non-biological9, biological covariates corresponding to normal, adenoma, and colon cancer samples can be specified and preserved to prevent over-normalization and loss of natural variance; a feature that was readily validated by UMAP.Following fRMA normalization, arrays from individual datasets were merged by matching probe.s Interstudy batch effects were identified by Uniform Manifold Approximation and Projection (UMAP) using the th percentile were filtered from the Meta-dataset using the genefilter function in the oligo (version 1.54.1) package20. Previous reports have suggested the utility of filtering datasets for low variant probes, especially for differential expression (DE) analysis as significance arising from low variance and not magnitude of change impedes meaningful data interpretation21. Finally, redundant probes were collapsed to their corresponding human gene symbol by maximum average expression using the hgu133plus2.db (version 3.2.3) package. Collectively, the constructed Meta-dataset contains a total of 705 samples including 231 normal, 132 adenoma, and 342 CRC tissue samples across 12 independent studies. A complete list of clinical and histological meta-data can be found in Table Next, probes with an expression variance in the lower 75TCGAbiolinks (version 2.18.0) package22. Data was pre-processed by within-lane normalization using the “GC content” option which includes loess robust local regression followed by global scaling and quantile normalization23. Thereafter, genes with row averages less than 0.25 were filtered out of the dataset and results were returned as counts per million (CPM). Finally, the TCGA dataset was prepared for DE analysis using the voom function and annotated to human gene symbol from ensemble ID.Raw data from TCGA-COAD dataset was downloaded using the getGEO function in the GEOquery package. Redundant probes were collapsed and annotated to human gene symbols prior to analysis.Data from GEO studies not using the GPL570 platform but still meeting the other inclusion criteria were also used for Meta-dataset validation and downloaded in their pre-processed state using the ggplot function within the ggplot2 (version 3.3.3) package24. To determine the overall effect of batch correction on the Meta-dataset, variance stability was compared between batch corrected and batch uncorrected Meta-datasets. Column (array) variances were determined using the colVars function within the Rfast (version 2.0.1) package25 and compared via boxplot to map cumulative distributions. Statistical analysis was performed using a Wilcoxon signed-rank test.UMAP was performed to identify batches, validate their removal, and ensure the preservation of biological signature post-batch correction. UMAP was run using twenty nearest neighbors for each for pre- and post-batch corrected data after which coordinates from the top two UMAP components were extracted to be visualized and color-coded either by study or sample using the 26. Specifically, sample types were predicted based on their UMAP clustering coordinates from 1,000 bootstraps of the density-based UMAP (DBU) cluster algorithm27 within the fpc (version 2.2.9) package28 with the reachability distance epsilon (eps) set to 0.45 and reachability minimum number of points (MinPts) set to 5 . Co-clustering was then determined by comparing the consensus sample predictions to the actual sample types and presented via confusion table.To assess the consistency of clustering results and its potential impact on downstream analyses, we performed unsupervised density-based consensus clustering13. In order to accomplish this, the LogFC of genes across adenoma versus normal (AvN), CRC versus adenoma (CvA), and CRC versus normal (CvN) comparison groups were computed using the limma (version 3.46.0) package. For comparing the Meta-dataset (microarray) with the TCGA-COAD dataset (RNAseq), the latter was voom-transformed prior to enumerating LogFC values. Cross-platform correlation between the LogFC values of common genes was then performed using the Spearman correlation coefficient to assess overall relationships.Cross-platform correlation was used to quantitatively validate the Meta-dataset. Because gene expression results obtained from different array platforms cannot be directly compared, we opted instead for comparing the log2 fold change (LogFC) values, or expression trends of genes common between the Meta-dataset and six external datasets. Previous studies have shown this method to be a robust means of validation as global gene expression trends are generally preserved across tissue types despite sample and study heterogeneitylimma. Specifically, DE analysis was independently performed on the top 25% most variable genes between each group. Genes were considered DE if they met the uniform threshold of having a |LogFC| ≥ 1.0 and False Discovery Rate (FDR) q-value < 0.01. Comparison of differentially expressed genes (DEGs) between each contrast was then visualized by a Venn-diagram using the VennDiagram (version 1.6.20) package29.Differential expression (DE) analysis for AvN, CvA, and CvN comparison groups was carried out using GSVA (version 1.38.2) package with default parameters30. Specifically, all C2 curated gene sets , which includes canonical pathways, KEGG, BIOCARTA, and REACTOME annotations, was downloaded from MSigDB and used for enumerating PE scores from the top 25% variable genes as before. A minimum gene set size was set to 10 genes while the maximum size was set to 1,000 genes. Differentially enriched pathways (DEPs), or those with a |LogFC| ≥ 0.25 and FDR q-value < 0.01, were identified using limma. As before, a Venn-diagram was used to visualize both common and unique pathways amongst each contrast.Pathway enrichment (PE) analysis for AvN, CvA, and CvN comparison groups was carried out by gene set variance analysis (GSVA) using the topGO (version 2.42.0) package31. Specifically, analysis was restricted to GO terms of more than 20 genes and statistical significance was determined by Fisher’s Exact test of gene ratios, or the number of observed enriched genes compared to the number of genes expected to be enriched by chance. Results were visualized as dot plots of the top 14 significant GO terms associated with up- and downregulated DEGs.Gene Ontology (GO) analysis was performed both as a means of validating enriched pathways and to visually represent up- and downregulated biological processes characteristic of adenoma formation and malignant transformation. DEGs identified in the AvN and CvA groups were compared to the full list of genes obtained after collapsing and annotating redundant probes. GO terms relating to biological processes were identified using the Prediction-based IPA analysis was used to functionally validate the Meta-dataset in an unbiased way. IPA utilizes advanced literature search techniques from a curated database to predict regulators, mechanistic networks, and sample characteristics based upon the magnitudes and directions of DEG LogFC values. We therefore used IPA to predict characteristics and potential mechanisms of AvN, CvA, and CvN comparison groups to validate that our Meta-dataset indeed represents each sample and potentially their progression. To do this, the list of DEGs between each pairwise comparison determined previously were used for analysis.q-values (determined using Fisher’s Exact Test) were used to validate that the signatures corresponded to the correct tissue type and state. Afterwards, the top regulatory networks corresponding to adenoma formation (AvN) and malignant transformation (CvA) were assessed and visualized. Information from the regulatory network including which upstream regulator and downstream pathway was predicted to be active or inhibited was then compared both to the GO analysis  and the literature (for assessing whether results are congruent with what is known).First, three disease and function predictions and their corresponding FDR 32. Datasheets used throughout the R code script to perform all the analyses can be found at figshare33. All dataset used in constructing the Meta-dataset as well as its technical validation can be found at GEO (https://www.ncbi.nlm.nih.gov/geo/) or Genomic Data Commons  and include: GSE418334, GSE867135, GSE934836, GSE1596037, GSE2091638, GSE2151039, GSE2259840, GSE2319441, GSE2387842, GSE3232343, GSE3311344, and GSE3736445, TCGA-COAD6, GSE4165746, GSE5011447, GSE6846848, GSE10017949, and GSE11760650.The Meta-dataset and associated clinical meta-data data are available at ArrayExpress51. In addition, sessile serrated adenoma samples were excluded when possible. Furthermore, studies used to construct the Meta-dataset were restricted to those using the GPL570 Affymetrix platform to reduce inter-platform batch effects and enable probe-probe matching when merging. Although the rigor of this pipeline substantially reduced the total number of candidate studies, we believe minimization of batch effects was a justified trade-off.An analytic pipeline was used in conjunction with inclusion/exclusion criteria for selecting microarray datasets suitable for merging Fig. . To ensu36, substantial heterogeneity in global probe expression exists between patient microarrays  and 221 CRC samples . This also included 191 subjects being designated M0, 36 M1, and 44 subjects having recurrent disease. Finally, anatomical location was provided in some of the datasets resulting in the representation of 9 cecal, 21 ascending colon, 11 transverse colon, 16 descending colon, 74 sigmoid colon, 2 rectosigmoid, and 36 rectal samples. Information regarding other clinicopathologic characteristics and/or treatment status were not reported as they were not explicitly detailed in the meta-data of the studies.Collectively, the Meta-dataset consists of 231 normal (32.8%), 132 adenoma (18.7%), and 342 colon cancer (48.5%) samples for a total of 705 samples. Based on the data available from the annotations provided by each of the 12 included studies, tissue from 179 males (25.4%), 194 females (27.5%), and 332 unidentified genders (47.14%) are represented. The overall mean age was 66.37 ± 11.46 y/o  and stabilized array variances as reflected by compressed boxplot interquartile ranges  and corrected (fRMA + ComBat) Meta-datasets were compared. As expected, batch correction significantly reduced  values for each pairwise comparison using limma. LogFC values of genes common with the Meta-dataset were then correlated using Spearman correlation coefficient (Rs) to account for the potential of non-linear relationships. All results, including the number of samples for each comparison and the total number of correlated genes, are detailed in Table Quantitative validation was performed by cross-platform correlation of common genes between the Meta-dataset and six external datasets of varying platforms detailed in Table Rs of 0.81 ± 0.07 for AvN, 0.70 ± 0.08 for CvA, and 0.82 ± 0.07 for CvN groups. First, we noted that cross-platform correlation of CvA gene trends was the lowest across all studies. We believe that this is a result of more substantial tissue heterogeneity that exists for adenoma and CRC samples relative to healthy tissue. In contrast, we found that comparison of CvN gene trends produced the strongest correlations in almost all cases with the greatest association (Rs = 0.90) observed between the Meta-dataset (microarray) and voom-transformed TCGA-COAD (RNAseq) datasets, despite their vastly different approaches to expression profiling. This finding was not completely unwarranted as another study comparing merged microarray meta-datasets to the TCGA-LUAD and TCGA-LUSC lung cancer datasets observed cross-platform correlation values of 0.92 and 0.93, respectively13. In fact, we found that correlation performance was independent of platform type with no differences observed between Affymetrix- or Agilent-based platforms, providing further evidence of the robust nature and general applicability of our Meta-dataset.Overall, we observed strong gene trend correlations between our Meta-dataset and all external datasets with an average q-value < 0.01 between AvN, CvA, and CvN comparison groups  were DEGs in our Meta-dataset’s CvN group with the exception of EIF3B. Similarly, Xingjie et al. 2016 constructed a 3-study meta-dataset and identified 7 important hub genes contributing to the development of CRC54. As before, a majority  were differentially expressed in our Meta-dataset excluding MMP9.To provide an additional degree of quantitative validation, we first identified DEGs, or genes with an absolute fold change of at least 2 LogFC ≥ 1) and FDR  and FDR et al.55. This group not only created a 4-study meta-dataset representing all three sample types, but also provided a detailed list of DEGs for each pairwise comparison. We noted excellent concordance between our Meta-dataset with a 93% (127/137), 89% (23/26) and 100% (172/172) overlap of DEGs across AvN, CvA, and CvN contrasts, respectively. Finally, we opted to compare CvA DEGs with those identified by Druliner et al. 2019 as their investigation is one of only a handful providing insight into transcriptome-wide changes driving malignant transformation of adenomas56. This study is unique in that expression profiles of cancer-free and cancer-associated polyps were compared to identify genes directly linked with adenoma neoplastic progression. Overall, we found that a substantial number of these driver genes, including GREM1, CXCL5, PLAU, IGF1, IGF2, and EREG were identified as CvA DEGs in our Meta-dataset.To validate adenoma-based signatures, DEGs in the AvN and CvA groups were compared to those reported by Hauptman Collectively, the strong inter-platform correlation of gene trends combined with the high degree of DEG overlap across a variety of independent studies demonstrates the robust nature of our Meta-dataset and suggests that results obtained from in-depth analyses have the power and consistency necessary for pre-clinical modelling of CRC neoplastic progression.In addition to quantitative validation, we also functionally validated AvN, CvA, and CvN signatures through integrative use of enrichment- and prediction-based analyses. This was done to provide both continuity and context to the quantitative validation results while also demonstrating the potential utility of our Meta-dataset for unearthing key genes, regulators, and pathways associated with early and late phases of colorectal neoplastic progression. Because transcriptome-wide changes are expected to be unevenly distributed along the neoplastic progression axis, we first investigated the degree of overlap between DEGs of each signature Fig. , top. Oflimma30. Likewise, we assessed the degree of overlap of DEPs which were defined as |LogFC ≥ 0.25|and FDR q-value < 0.01  while the top negatively associated pathway was SABATES_COLORECTAL_ADENOMA_DN , both of which are derived from GSE867135. Moreover, well known culprits of early neoplastic progression including pathways associated with epithelial-mesenchymal transition (EMT), DNA damage and repair, MYC activation, and hyperproliferation via cell cycle transition are AvN-associated DEPs. In the same light, pathways known to be closely associated with CRC pathogenesis such as TGFβ157, FOXM158, MYC59, angiogenesis and extracellular matrix (ECM) remodelling60 are CvA and CvN-associated DEPs. Importantly, DEPs were recapitulated by GO analysis of DEGs and collectively show that adenoma formation is marked by loss of cellular functions characteristic of differentiated tissue as well as hyperproliferation in response to genomic stress and potentially inflammation  on the top 25% variable genes for each comparison group as well as Gene Ontology (GO) on DEGs. GSVA scores measures gene set enrichment variation across the entire dataset and provides sample-level enrichment scores based on the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm, enabling accurate identification of differentially enriched pathways (DEPs) using linear modelling techniques such as .01 Fig. , bottom.ion Fig. , left. Iion Fig. , right.5. To functionally validate AvN, CvA, and CvN signatures, we predicted associated disease and functions from DEGs. The analysis was based on the direction of the LogFC and FDR values and was restricted to drawing direct relationships from the human database. IPA correctly identified the adenoma signature from the AvN DEG list with Colorectal Adenoma (FDR = 7.79 × 10−79), Adenoma (FDR = 8.65 × 10−61), and Benign Tumor (FDR = 1.47 × 10−55) being top results , Colorectal Cancer (FDR = 2.85 × 10−24), and Large Intestine Neoplasm (FDR = 1.39 × 10−17) being predicted from the CvN DEG list. Top disease and functions associated with the CvA signature were in line with functional enrichment results. For mechanistic networks, we found that adenoma formation was associated with the activation of known CRC oncogenes FOXM158 and TP6361, which was predicted to be primary regulators governing the activation of interphase cell cycle transition and DNA repair pathways 64 as well as leukocyte chemotaxis and adhesion, presumably culminating in tumor infiltration  software to identify key sample characteristics and mechanistic pathways as a secondary functional validation metric. IPA is a powerful tool that utilizes a curated database of scientific literature to predict regulators, pathways, and associated disease and functions from a list of genes and their corresponding LogFC and/or significance levellts Fig. . This waays Fig. , left. Oion Fig. , right. in silico analyses, biomarker discovery, pre-clinical modelling, and even hypothesis generation and testing. Of course, by making our Meta-dataset openly accessible, we invite the scientific community to apply novel tools and techniques to further dissect mechanisms associated with adenoma formation and malignant transformation.In the present study, we aggregated 705 arrays across 12 independent studies to create a Meta-dataset of normal, adenoma, and colon cancer samples for the primary goal of studying colorectal neoplasm formation and progression. Collectively, results from transcriptional profiling of early and late stages of neoplastic progression using our Meta-dataset not only demonstrated results that were generalizable across a variety of studies and array platforms, but also strongly agreed with the literature, thereby validating its accuracy and robustness. Moreover, by containing a breadth of adenoma samples our Meta-dataset provides distinct advantages over the conventional TCGA-COAD dataset, especially for investigating pre-malignant phases of colorectal neoplastic progression. It is our belief that this Meta-dataset provides a powerful public tool to facilitate further in-depth Supplementary File 1Supplementary File 2


Mice, especially A/J mice, have been widely employed to elucidate the underlying mechanisms of lung tumor formation and progression and to derive human-relevant modes of action. Cigarette smoke (CS) exposure induces tumors in the lungs; but, non-exposed A/J mice will also develop lung tumors spontaneously with age, which raises the question of discriminating CS-related lung tumors from spontaneous ones. However, the challenge is that spontaneous tumors are histologically indistinguishable from the tumors occurring in CS-exposed mice. We conducted an 18-month inhalation study in A/J mice to assess the impact of lifetime exposure to Tobacco Heating System (THS) 2.2 aerosol relative to exposure to 3R4F cigarette smoke (CS) on toxicity and carcinogenicity endpoints. To tackle the above challenge, a 13-gene gene signature was developed based on an independent A/J mouse CS exposure study, following by a one-class classifier development based on the current study. Identifying gene signature in one data set and building classifier in another data set addresses the feature/gene selection bias which is a well-known problem in literature. Applied to data from this study, this gene signature classifier distinguished tumors in CS-exposed animals from spontaneous tumors. Lung tumors from THS 2.2 aerosol-exposed mice were significantly different from those of CS-exposed mice but not from spontaneous tumors. The signature was also applied to human lung adenocarcinoma gene expression data (from The Cancer Genome Atlas) and discriminated cancers in never-smokers from those in ever-smokers, suggesting translatability of our signature genes from mice to humans. A possible application of this gene signature is to discriminate lung cancer patients who may benefit from specific treatments . Mutational spectra from a subset of samples were also utilized for tumor classification, yielding similar results. “Landscaping” the molecular features of A/J mouse lung tumors highlighted, for the first time, a number of events that are also known to play a role in human lung tumorigenesis, such as Lrp1b mutation and Ros1 overexpression. This study shows that omics and computational tools provide useful means of tumor classification where histopathological evaluation alone may be unsatisfactory to distinguish between age- and exposure-related lung tumors. The carcinogenic risk of a chemical is traditionally assessed in 2-year rodent carcinogenicity assays selecting the relevant route of administration for the compound to be tested. Despite concerns regarding the use of large numbers of animals, exposures that are not relevant to humans, and the sometimes poor translatability to human outcomes, not to mention prohibitive costs and time needed Cohen, , the 2-yWe wanted to further explore the molecular differences in proliferative lung lesions from another chronic toxicity/carcinogenicity study in A/J mice, in which animals were not only exposed to CS but also to an aerosol from the Tobacco Heating System (THS) 2.2. Because THS 2.2 aerosol contains significantly lower levels of harmful and potentially harmful constituents than CS  were whole-body exposed to aerosol from THS 2.2 at 3 test atmosphere concentrations of nicotine  or to 1 concentration of 3R4F CS (13.4 μg nicotine/L test atmosphere) in whole-body inhalation chambers for 6 h per day, 5 days per week. The nicotine concentration in THS (M) matched that in CS; the CS concentration was chosen on the basis of prior data indicating a robust lung tumor response in this mouse strain  protocol in a facility licensed by the Agri-Food & Veterinary Authority of Singapore (AVA) and accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC), where the procedures for care and use of animals for scientific purposes were in accordance with the NACLAR Guidelines (NACLAR 2004). Additional details about the study design, animal husbandry, aerosol generation, animal exposure, and monitoring are provided in the N = 8, 10–12, and 10–13, respectively, per treatment group), and at terminal dissection from male animals .Lung tumors in A/J mice begin to develop at around month 5 . The whole lung with trachea and larynx was then removed from the animal, rinsed with sterile PBS, blotted dry, and placed in a sterile petri dish. The trachea was cannulated using an 18G catheter, and lungs were inflated slowly with 50% (v/v) Tissue-Tek® optimum cutting temperature (OCT) compound  in sterile PBS at a rate of ~0.1 mL per 10 s from a syringe. The volume of 50% (v/v) OCT/PBS required to fully inflate a lung was ~1–1.5 mL and dependent on the size of the animal. When each lung lobe was fully inflated, the bronchus leading to each lobe was clamped with forceps, and each lobe was dissected and placed individually into a disposable Tissue-Tek Cryomold® (InLab Supplies Pte Ltd) prefilled with OCT compound. The filled Cryomolds® were placed into isopentane precooled with liquid nitrogen, and frozen tissues were stored at ≤-70°C until further processing.Animals from each group were necropsied within 16–24 h of the last exposure and subjected to gross pathology examination. Lungs were perfused Laser-capture microdissection (LCM) was used to specifically collect lung parenchymal tissue (“parenchyma”) or tissue from each identified proliferative lung lesion  under the guidance of the study pathologist. To do so, serial lung cryosections at 20 μm distance were placed, 3 consecutive sections at a time, on sterilized, RNase-free membrane slides . Slides were transferred immediately for fixing and staining with 1% (w/v) cresyl violet . Stained, air-dried sections were then reviewed by the study pathologist, who identified proliferative lung lesions in each section. These lesions were subjected to LCM using the PALM MicroBeam (Carl Zeiss Microscopy LLC). LCM tissue samples were transferred to opaque AdhesiveCap 500 tubes (Carl Zeiss Microscopy LLC) and stored at −80°C until RNA and DNA extraction . In total, 172 parenchyma and 101 tumor samples were collected for gene expression analysis, and 172 parenchyma and 73 tumor samples were collected for DNA sequencing .Sample randomization was performed prior to RNA extraction as a complete block randomization, where the blocking factor was defined by both the type of exposure (study/treatment group) and the dissection time point. The purpose of block randomization is to blind the analysts who conducted RNA extraction and gene expression analysis, and to prevent potential confounding batch effect.Total RNA was isolated from the LCM tissues using the RNeasy Micro Kit  following the manufacturer's instructions for QIAcube (QIAGEN) automated extraction. The isolated RNA was subjected to quality control (QC) checks using the Agilent 2100 Bioanalyzer , and the quantity of the isolated RNA was determined using NanoDrop 1000 spectrophotometers . Because a pre-study optimization phase indicated that the RNA Integrity Number is not reliable in this particular sample type, sample quality was evaluated based on BioAnalyzer traces, and all RNA samples exhibiting typical ribosomal RNA peaks  were processed for the downstream microarray analysis.Two ng total RNA were processed using the Affymetrix® HT 3′-IVT Pico kit . The resulting double stranded cDNA was then hybridized to GeneChip® Mouse Genome 430 2.0 Arrays (Thermo Fisher Scientific) in a GeneChip® Hybridization Oven 645 (Thermo Fisher Scientific) according to the manufacturer's instructions. Arrays were rinsed and stained on a GeneChip® FS450 DX Fluidics Station (Thermo Fisher Scientific) using the Affymetrix® GeneChip® Command Console® Software . Finally, microarrays were scanned using a GeneChip® Scanner 3000 7G (Thermo Fisher Scientific). Raw images from the scanner were saved as DAT files, which were automatically gridded by the AGCC software to give Affymetrix CEL files.N = 7, 8, 8, 7, and 8 for sham, 3R4F, THS (L), THS (M) and THS (H), respectively, for parenchyma tissue]. Month 10 data included those from 10 to 4 parenchyma and tumor samples from the sham group, 7 and 5 parenchyma and tumor samples from the 3R4F group, 12 and 5 parenchyma and tumor samples from the THS (L) group, 11 and 4 parenchyma and tumor samples from the THS (M) group and 12 parenchyma samples from the THS (H) group. Month 18 data derived from 10 to 5 parenchyma and tumor samples from the sham group, 10 and 16 parenchyma and tumor samples from the 3R4F group, 10 and 9 parenchyma and tumor samples from the THS (L) group, 13 and 12 parenchyma and tumor samples from the THS (M) group and 12 and 9 parenchyma and tumor samples from the THS (H) group. The month 15 samples from the male animals included 16 and 8 parenchyma and tumor samples from the sham group and 5 and 2 parenchyma and tumor samples from the THS (H) group.The raw CEL files were background-corrected, normalized, and summarized using the frozen robust multiarray analysis  and tumor (T) samples from a previous inhalation study (accession number: E-MTAB-1871) were analyzed for interaction effects between tissue type (T or P) and between air/sham and CS exposure using a linear model , as a single model may not fulfill homoscedasticity conditions.with i = 1, …, p and j = 1, …, n, where p denotes the total number of genes, 17,473; n denotes the number of independent samples (mice) in the above model; ExposureType is 3R4F CS (13.4 ug/l nicotine), or THS 2.2 Low (6.7 μg/l nicotine), or THS 2.2 Med (13.4 μg/l nicotine), or THS 2.2 High (26.8 μg/l nicotine), respectively; βp-values of the interaction coefficient were corrected applying the false discovery rate (fdr) method, and adjusted p-values below 0.05 were considered significant. Because there was only 1 spontaneous tumor sample, which did not pass the QC, from month 5, the interaction analysis was restricted to samples from dissection months 10, 15, and 18. The resulting interaction terms were displayed as volcano plots in x-axis represents the estimated effect (the interaction coefficient), and the y-axis represents the –log10 for each gene.The interaction model was not fitted separately for every gene, but was fitted by using the popular R package limma which is widely used in gene expression analysis. limma uses moderated t-statistic (t), which is the ratio of the M-value to its standard error. The moderated t-statistic has the same interpretation as an ordinary t-statistic except that the standard errors have been moderated across genes, effectively borrowing information from the ensemble of genes to aid with inference about each individual gene . The size of the gene signature is denoted by N. The probability distribution of the spontaneous tumor is described as a multivariate Gaussian distribution, f , as follows:To identify a specific tumor gene signature that discriminates between spontaneous tumors in sham animals and those that were exposure-related, the above statistical model was applied to group MS-300 for data in the previous A/J mouse study  distance to the mean (u) of the sham group is evaluated by computing the Mahalanobis distance:The Mahalanobis distance method was used as a 1-class classifier. For any new sample xj as a spontaneous tumor if the latter is sufficiently small (please refer to the below classification rule).This distance thereby enables the classification of any sample 2 distribution is smaller than 0.05, the tumor was believed to belong to the non-spontaneous group, otherwise, the tumor sample would be classified as spontaneous tumor. The distances were estimated for all samples, and the mean distances were displayed as a bar plot . RNA-Seq data for human lung adenocarcinoma samples were obtained from The Cancer Genome Atlas Program . Tumor samples in TCGA data were filtered out if their diagnosis was not “Lung Adenocarcinoma,” if the information from the “tobacco_smoking_history” and “tobacco_smoking_history_indicator” fields were not consistent, or if the content of column “tobacco_smoking_history” was either empty or listed as “Current Reformed Smoker, Duration Not Specified.” We thus retrieved data from 205 tumor samples from 45 current smokers, 130 former smokers, and 30 never-smokers.The human orthologs of the mouse gene signature were obtained using HGNC Comparison of Orthology Predictions (HCOP)  of the Mahalanobis distance of the 3R4F group/current smoker group minus the third quartile (Q3) of the Mahalanobis distance of the sham group/never-smoker group was estimated for the current A/J mouse and TCGA datasets, respectively. Again 10,000 random re-samplings were performed to obtain the bootstrapped p-values.The ranks of signature genes in this A/J mouse study and the TCGA human lung adenocarcinoma gene expression data were computed as follows. The interaction terms for the A/J 3R4F group at month 18 were sorted in descending order based on their absolute values. Comparisons of the signs of the signature gene interaction terms in the current A/J mouse study with those in the previous study confirmed that they are 100% consistent. Then, the interaction terms in the TCGA dataset were computed and sorted in descending order based on their absolute values. The signs of the interaction terms of the signature genes in the TCGA dataset were also compared with those in the previous A/J mouse study, and they are 85% consistent. Next, the median ranks of the signature genes in the current A/J mouse study were calculated. To estimate their The cancer outlier gene (COG) analysis reported by Seo et al.  was applSample randomization was performed prior to DNA extraction as a complete block randomization, where the blocking factor was defined by both the type of exposure (study group) and the dissection time point.DNA was isolated from the LCM tissues following the addition of 375 μL AMPure XP magnetic beads  to each sample and incubation for 15 min on a rotary shaker. The samples were then placed on a magnetic rack for 5 min. Two washes with 1,400 μL 70% ethanol were performed before eluting the captured DNA with 22.5 μL AE buffer . DNA quantity was assessed on a Qubit® 2.0 fluorimeter (Thermo Fisher Scientific). Two tumor samples failed DNA QC and were therefore excluded from further processing and analysis .DNA sequencing libraries were prepared using the Nugen Ovation® Ultralow Library Systems  following the manufacturer's instructions. The concentrations and sizes of the sequencing libraries were verified on the Agilent 2100 Bioanalyzer. Normalized libraries were pooled in multiplexes of libraries and clustered on Illumina HiSeq 3000/4000 PE flow cells using Illumina HiSeq 3000/4000 PE Cluster Kits . Sequencing was performed on an Illumina HiSeq 4000 system using Illumina HiSeq 3000/4000 SBS kits (300 cycles).Reads were cleaned of adapters and trimmed to a maximum length of 150 bases using the bbduk tool version 37.99  calling in areas of very high coverage. For the most densely sequenced female and male parenchyma tissue samples, the sequence coverage density distribution was determined with the SAMtools suite mpileup program. The following procedure was used to generate the mask for excluding high coverage regions: for the autosomes and the X chromosome, the 95th percentile of the read density distribution derived from the female parenchyma sample was chosen as the cut-off; for the Y-chromosome, the 95th percentile of the read distribution from the male parenchyma sample was chosen. Sites in the genome with more than the specified coverage were identified, and contiguous sites were joined to segments. These segments were filtered by a minimum length of 20 bp, extended on either side by 50 bp, before merging adjacent elements closer than 30 bp. The resulting exclusion list was inverted using the bedtools complement function  using the VariantAnnotation , and the distance matrix was used as the input for hierarchical clustering, as implemented in the R function hclust (the default complete linkage method was used).t-test with Welch modification to the degrees of freedom. Specifically, the R function t-test was used . Of these genes, 1 (Arsb) was upregulated and 6  were downregulated in tumors from CS-exposed animals compared to spontaneous tumors from sham animals  in the lung tumors of animals exposed to 3R4F CS for 18 months compared to sham animals  were downregulated in tumors from CS-exposed mice compared to those from sham-exposed mice. Similarly, marked effects on lipid or glycophospholipid metabolism pathways were not observed. The absence of statistically significant changes in gene expression levels in lung tumors from THS 2.2 aerosol-exposed mice precluded a similar analysis of affected pathways and biological processes.Our previous analysis of A/J mouse lung tumor gene expression profiles indicated a suppression of the humoral immune response in tumors from 3R4F CS-exposed animals, with an overall decrease in expression levels of genes contributing to the humoral immune response network and a predicted reduction in B cell function . In addition, based on this gene signature, lung tumors from female THS 2.2 aerosol-exposed mice were not significantly different from those in sham animals. They were, however, significantly different from those in 3R4F CS-exposed mice . The lung tumors from male A/J mice exposed to THS 2.2 aerosol also appeared to exhibit dissimilarities to tumors in sham and 3R4F CS-exposed mice. However, because the number of tumors in the male THS 2.2 aerosol-exposed mice was small (N = 2), the statistical test was not as powerful as that for the corresponding female study group  were 0 and 0.002 in the current A/J mouse and TCGA datasets, respectively . Former smokers exhibited similarities to both never- and current smokers with respect to the gene signature, with the median distance closer to the current smokers than to the never-smokers  were selected for downstream analysis of the total number, the frequency of base substitution, and their potential phenotypic effects.p < 0.05) as well as an increased average Mahalanobis distance to the sham group was the 3R4F CS exposure group , suggesting that point mutation is not the predominant cause for the proposed oncogene activation in 3R4F CS- or THS 2.2 aerosol-exposed mice. Lrp1b was the gene most frequently affected by point mutations, followed by Csmd1, Fgfr2, Grm7, Dcc, Fhit, and Csmd3. There was no obvious relationship between mutation frequency and type of exposure.Subsequently, the location of the mutations was determined relative to the genes, and a list of genes containing at least 1 mutation was generated. As the total number of exonic point mutations was low, any mutation location (including intronic mutations) was considered to affect genes. Surprisingly, Ros1 expression was frequently highly upregulated, independent of exposure, whereas Ddx3y, Kdm5d, and Uty gene expression was strongly increased in the same tumors from sham and THS 2.2 High aerosol-exposed mice.Overall, very few genes had protein function-altering point mutations in more than 1 of the tumor tissues sequenced here, preventing further conclusions to the potential phenotypic effects of these mutations. Therefore, the list of genes affected by mutations was combined with the list of genes considered to be gene expression outliers (COGs), and genes were filtered for their previously reported role(s) in human cancers. This yielded a transcriptional and mutation landscape, providing a unique insight into the molecular makeup of age- and exposure-related tumors in this mouse strain . Most noThe A/J mouse strain is susceptible to lung tumorigenesis following chemical exposure, including exposure to CS Coggins, . As thishttps://www.cancer.gov/tcga). This analysis showed that lung tumors in never-smokers could be discriminated from lung tumors in former and current smokers, indicating that both gene signature and classifier are robust and translatable from mice to humans.Given our prior experience with this lung cancer model, we utilized gene expression profiles of LCM lung tissues to develop a classification approach. Unlike previously, we argued that as exposed mice may exhibit both exposure-related and spontaneous tumors, distinguishing the 2 should be considered a 1-class rather than a 2-class problem, with the only unequivocally defined class being the spontaneous tumors arising in sham animals. One-class classification, which is also known as unary classification or class-modeling, aims at identifying samples of a specific group amongst all samples by learning from a training set containing only the objects of that group  will be necessary to further elucidate this observation.Based on the molecular tumor landscaping attempted here, it is tempting to speculate that other known cancer genes are involved in driving lung tumorigenesis in this mouse strain. For example, N = 50 per group). However, it was not possible  to include additional animal groups of that size to accommodate omics investigations. Since lung tumor incidence and multiplicity in THS 2.2 aerosol-exposed animals were not different from those in sham animals  and discriminated cancers in never-smokers from those in ever-smokers, suggesting translatability of our signature genes from mice to humans. This study shows that omics and computational tools provide useful means of tumor classification where histopathological evaluation alone is unsatisfactory to distinguish between age- and exposure-related lung tumors. The results of this study are promising and highlight not only the value of 1-class classifiers when tumor types cannot be easily characterized but also how omics and computational tools can be used to corroborate the relevance of the animal model to exposure effects in humans.https://www.ebi.ac.uk/arrayexpress/, E-MTAB-8540, https://www.ebi.ac.uk/ena, PRJEB34661. Further details on the datasets, the protocols, and additional data visualizations are available on the INTERVALS™ platform at https://doi.org/10.26126/intervals.j3slv2.2.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: The animal study was reviewed and approved by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC).YX, FM, JB, and NS performed the computational analysis. KL and EW designed the experiment and interpreted the computational analysis results. All authors contributed to the article and approved the submitted version.YX, KL, FM, JB, KT, LN, EW, EG, RD, DP, DB, SO, NS, NI, PV, JH, and MP are employed by Philip Morris International (PMI). AB is an employee of Histovia GmbH, who was contracted and paid by Philip Morris International (PMI).


Is the exhaled breath test feasible and accurate to detect lung cancer using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS)?In this diagnostic study, alveolar air was collected from 139 patients with lung cancer and 289 healthy participants. The breath test based on HPPI-TOFMS reached a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and area under curve of 0.9586 in the validation data set.These findings suggest that an exhaled breath test with HPPI-TOFMS may be a promising approach for lung cancer detection. This diagnostic study examines whether a breath test that uses high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) can accurately distinguish patients with lung cancer from those without. Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People’s Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020.Breath testing and SVM algorithm.The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC).Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586.This diagnostic study’s results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings. Gas-chromatography mass spectrometry (GC-MS) is a well-established technique to detect volatile organic compounds (VOCs) in exhaled breath, but the tedious pretreatment steps and time-consuming detection process limit its application.15 Electronic noses can be used to perform exhaled breath analysis, but they are not able to quantify the absolute content of VOCs in a mixture.16 Direct mass spectrometry, such as secondary electrospray ionization,17 selected-ion-flow-tube,18 and proton-transfer-reaction,19 has been used for rapid detection of exhaled breath; however, the vast amount of water vapor in exhaled breath makes the ionization process more intricate and increases the complexity for data analysis.20 High-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) is a promising tool for breath testing, because it is highly sensitive, does not require pretreatment of exhaled breath, and holds great tolerance for humidity.21 HPPI-TOFMS has successfully monitored the concentration of exhaled propofol during surgery and has shown good association with blood propofol concentration and bispectral index.23Exhaled breath may be a better tool for cancer detection because of its noninvasive nature.14 previous studies on breath tests have had substantial limitations and potential bias, such as small sample size, poor methodological quality, and lack of validation. In our case-control diagnostic study, we investigated whether a breath test combining HPPI-TOFMS and a support vector machine (SVM) algorithm was able to distinguish patients with lung cancer from healthy individuals.As shown by Hanna et al,STARD) reporting guideline.24 A prospective-specimen collection, retrospective-blinded evaluation (PROBE) design25 was used, and the overall study design is shown in This diagnostic study followed the Standards for Reporting of Diagnostic Accuracy  aged 18 years or older, (2) no history of cancer within 5 years and no anticancer treatment, (3) no sign of active infections, and (4) no sign of liver or kidney dysfunction. A total of 289 participants without pulmonary noncalcified nodules were selected and served as the control group.Patients with pulmonary lesions and who had undergone thoracic surgery or endobronchial ultrasonography-guided transbronchial needle aspiration (EBUS-TBNA) were consecutively recruited at the Department of Thoracic Surgery, Peking University People’s Hospital. Patients were recruited with the following criteria: (1) aged 18 years or older, (2) pulmonary lesions seen on computed tomography (CT) images, and (3) plan to undergo thoracic surgery or EBUS-TBNA. Patients who met the following criteria were excluded: (1) history of other types of cancer within 5 years, (2) received anticancer treatment before, (3) pathologically confirmed lung benign diseases, (4) signs of active infections, or (5) liver or kidney dysfunction. A total of 139 patients were identified with pathologically diagnosed lung cancer. For all participants, the clinical data and demographic characteristics were collected from medical records and questionnaires.Spiral CT images were obtained using a 64-detector CT row scanner with a low-dose setting  and were reconstructed in overlapping contiguous 5-mm increments at a pitch of 1.25 mm. The LDCT images of all included participants were first analyzed by an artificial intelligence–based program σ-Discover Lung , and then confirmed by an experienced radiologist (Y.G.).All exhaled breath samples were collected by trained investigators following the same protocol. Exhaled breath samples were collected in the morning for all participants. For patients with lung cancer, it was collected the next morning after admission to hospital. For healthy individuals, exhaled breath was collected at the same day of physical examination and before LDCT examination. All participants fasted for at least 8 hours. To reduce potential confounding factors, all participants were asked not to ingest spicy food, alcohol, or coffee the night before exhaled breath collection.2 sensor, and an interactive display screen, as shown in 2 sensor was used to ensure alveolar air was collected. Briefly, each participant took a deep inhalation and then exhaled slowly through the mask into the apparatus. Exhaled breath collection began once the CO2 sensor detected that the CO2 concentration exceeded 4%.Home-designed sampling equipment was used for breath sampling, and the breath sample was stored using Tedlar (DuPont de Nemours) air bags. The sampling equipment was composed of a disposable face mask, a cooling module, a COParticipants first gargled with pure water, then performed a single deep nasal inhalation followed by complete exhalation via their mouth into the air bag. At both clinical centers, breath samples were collected in a fixed room, and the room air was also collected before and after sample collection of participants. All air bags were delivered to the laboratory and tested within 4 hours. Exhaled breath was collected before LDCT scanning for the control group and before surgery or EBUS-TBNA for inpatients. The data analysis team were blind to the clinical diagnosis and clinical team performing surgery were also blind to the breath tests.26 The HPPI-TOFMS consisted of a vacuum ultraviolet lamp–based HPPI ion source and an orthogonal acceleration time-of-flight (oa-TOF) mass analyzer, and the TOF mass analyzer had a mass resolution of 4000  at mass-to-charge ratio (m/z) = 92, which was achieved with a 0.4 m field-free drift tube. The pressure in the HPPI ion source was set at 500 Pa, and 2 capillaries were arranged in the ion source. Gas-phase exhaled breath sample was directly introduced into the ionization region through a 250 μm inner diameter, 0.60 m long stainless-steel capillary from the air bag. To eliminate condensation of exhaled VOCs and minimize possible surface adsorption, the stainless-steel capillary was heated to 100 °C and the HPPI ion source was heated to 60 °C. The TOF signals were recorded by a 400 picoseconds time-to-digital converter rate at 25 kHz, and all the mass spectra were accumulated for 60 seconds. Mass spectrum peaks detected by HPPI-TOFMS with m/z less than 500 were recorded and 32 500 features were extracted from the HPPI-TOFMS data of each exhaled breath sample.The design and structure of HPPI-TOFMS has been reported before.w and bias b, which is treated as the classifier and fixed after training, with the training data set. Given a sample x, its prediction score y can be obtained by y = Tw × x + b. For the binary classification , the calling threshold is fixed as 0 to classify the positive and negative samples. For example, the sample can be classified as having lung cancer if y is less than or equal to 0, or vice versa. For the SVM, the linear kernel is applied to train the model.The core of this work is to distinguish patients with lung cancer from healthy individuals, which can be treated as a binary classification problem. In machine learning, the binary classification problem can be solved by the regression model or classification model. In this work, we choose the classification model SVM to distinguish between patients with lung cancer and healthy individuals. The core of SVM is to infer the weight C is a critical parameter to constrain a penalty for the misclassified samples. Therefore, we first performed 500 iterations of a 4-fold cross-validation on the whole discovery data set, in which 381 samples were randomly divided into a training set with 286 samples , and a test set with 95 samples . During each training, the sqrt (square root) normalization is applied as the preprocessing strategy to normalize the original data. An SVM model was used to evaluate the mean classification accuracy at 500 iterations for different parameter C values. The detection accuracy is unrelated to the parameter C. Therefore, we set C = 1.0 in this study, which is the default value in the SVM model. By setting C = 1.0, the final model was trained and tested on whole discovery data set (381 samples) to construct the detection model and evaluated on the final validation data set (47 samples). The code and supplemental data set have been uploaded to GitHub.For the SVM, the regularization parameter t test or Fisher exact test. Two-sided P < .05 was considered significant. All statistical analyses were performed using SPSS software version 24.0 (IBM) from August to October 2020.Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated to evaluate diagnostic performance of the breath test. Receiver-operating characteristic curve (ROC) and precision recall curve were performed and area under ROC (AUC) was also calculated to evaluate classification performance of the breath test. Baseline characteristics were analyzed with independent t = 3.98; P < .001). Most patients with lung cancer (126 of 139 patients) were at early stage (TNM stages I and II).A total of 139 patients with lung cancer and 289 healthy individuals were included in this study. Of all participants, 228 (53.27%) were women, and the mean (SD) age was 57.0 (11.4) years. We collected exhaled breath samples of healthy individuals and patients with lung cancer from March 1 to September 1, 2019. After diagnoses of all participants were ascertained, all samples were randomly assigned to the discovery data set and validation data set. The baseline characteristics of all eligible participants are shown in All invited participants agreed to participate in the study with a patient acceptability rate of 100%, and we performed successful sample collection for all participants. The exhaled breath collection process took approximately 60 seconds for a participant. No adverse events were observed during breath sample collection.As shown in According to PROBE design, 47 participants were assigned to the blinded validation data set. The model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586 in this blinded validation data set. As shown in In this study, we demonstrated that the HPPI-TOFMS breath test is feasible in clinical practice. We have also established that a model for lung cancer detection was associated with high sensitivity and specificity.28 According to Hanna et al,14 these studies had small sample size and low methodological quality. We have made many efforts to overcome these shortages. First, we used a PROBE design in this study. Exhaled breath was collected and tested before LDCT scanning and pathological diagnosis of lung cancer; therefore, the clinical outcome was unknown for the whole research team. After recruitment of all eligible participants, the samples were randomly assigned into the discovery and validation data sets, and the data analysis team was blind to the clinical outcome. By collection and testing breath samples prospectively and analyzing data retrospectively and blinded, we maximally controlled for potential selection bias. Second, the most common bias in biomarker research is the systematic differences between case and control population, and we have improved many methodological details to ensure standardized sample collection and minimize measurement bias. We have (1) set up a sample collection team who were trained and followed the same protocol; (2) designed sampling equipment that included a CO2 sensor to ensure alveolar air was collected and minimized individual variances; and (3) exhaled breath was collected in a fixed room in each clinical center and room air was also collected before and after sample collection to reduce environmental factors. Third, a discovery data set and a blinded validation data set for rigorous evaluation of classification accuracy are essential for development of biomarkers,30 whereas most studies in lung cancer include only 1 stage and they have small sample size. Therefore, the designed validation data set improves the quality and robustness of our study.On the basis of its noninvasive nature and high acceptability, breath testing is considered an improved tool for diagnosis and screening. Many studies have investigated the diagnostic efficacy of exhaled breath for lung cancer.31 These features make HPPI-TOFMS hold potentially great value for clinical application.Compared with GC-MS, HPPI-TOFMS does not require sample pretreatment or VOC enrichment and it takes only 60 seconds to analyze a sample. HPPI-TOFMS enhances resolution for more precise identification and quantification of VOCs. In addition, HPPI is one of the most powerful and popular soft ionization techniques for online monitoring of trace VOCs, because of its high ionization efficiency, high molecular ion yield, and low degree of fragmentation.33 Although the blinded validation data set was small, the BreLC v1.0 showed a high positive predictive value of 90.48%, which indicates that BreLC v1.0 may be a promising candidate tool for lung cancer screening. Shlomi et al2 also reported that volatile organic compounds in exhaled breath could discriminate patients with lung cancer with EGFR variant from those harboring wild-type EGFR with an accuracy of 83%. This evidence suggests promising clinical application of breath testing. Moreover, most of the characteristic ions of HPPI were molecular ions or quasi-molecular ions with little clusters, which is beneficial for mass spectral interpretation.34The performance of LDCT for lung cancer screening is not satisfactory so far, and the positive predictive value is approximately 8%, according to a systematic review.Limitations of this study should also be noted. First, different from previous studies with GC-MS or sensors, the electronic nose for example, we developed the BreLC v1.0 model on the basis of features extracted from HPPI-TOFMS data but not VOCs. This disadvantage makes it difficult to replicate the model with other platforms, and we will further refine our mass spectrometry and establish a feasible and VOC-based detection model. Second, the blinded validation data set is small, and the model was not validated with external data from independent clinical centers. Further multicenter clinical studies will be conducted to validate the model.This diagnostic study’s results suggest encouraging findings that breath testing may be a reliable approach to lung cancer detection and HPPI-TOFMS may provide fast and precise detection of exhaled breath. Exhaled breath holds promising clinical application in lung cancer screening.