An increasing volume of evidence suggests that SETDB1 plays a role in the tumorigenesis of various cancers, classifying SETDB1 as an oncoprotein. However, owing to its numerous protein partners and their global-scale effects, the molecular mechanism underlying SETDB1-involved oncogenesis remains ambiguous. In this study, using public transcriptome data of lung adenocarcinoma (ADC) and squamous-cell carcinoma (SCC), we compared tumors with high-level SETDB1 (SH) and those with low-level SETDB1 . The results of principal component analysis revealed a transcriptomic distinction and divergence between the SH and SL samples in both ADCs and SCCs. The results of gene set enrichment analysis indicated that genes involved in the “epithelial–mesenchymal transition,” “innate immune response,” and “autoimmunity” collections were significantly depleted in SH tumors, whereas those involved in “RNA interference” collections were enriched. Chromatin-modifying genes were highly expressed in SH tumors, and the variance in their expression was incomparably high in SCC-SH, which suggested greater heterogeneity within SCC tumors. DNA methyltransferase genes were also overrepresented in SH samples, and most differentially methylated CpGs (SH/SL) were undermethylated in a highly biased manner in ADCs. We identified interesting molecular signatures associated with the possible roles of SETDB1 in lung cancer. We expect these SETDB1-associated molecular signatures to facilitate the development of biologically relevant targeted therapies for particular types of lung cancer. Lung cancer, which is the leading cause of cancer-related morbidity and mortality worldwide , can be Lung cancer is a molecularly heterogeneous disease, and grasping the underlying biology is essential to develop effective therapies against it. This is especially the case if the relatively higher mutation rates in NSCLC are considered; the mutation frequency has been shown to be 3.5 and 3.9 per megabase (Mb) in ADC and SCC, respectively, and these values are approximately twofold higher than the mean rate of 1.8 per Mb across all tumor types . A high Human cancer genomics studies have recognized that point mutations, translocations, deletions, and gene amplification events frequently occur in genes encoding histone-modifying enzymes such as histone methyltransferases, demethylases, acetyltransferases, and deacetylases . One of Although the findings of studies on SETDB1 have consistently indicated its oncogenic role in various cancers, the broad ranges of genomic targets and protein partners of SETDB1 , along with their global effects, make it challenging to prove an immediate connection of SETDB1 with certain tumors. Therefore, the molecular mechanism of SETDB1-involved oncogenesis remains largely unknown. In this study, based on the SETDB1 levels, we attempted subtyping of the lung ADC and SCC samples to measure how SETDB1 overexpression quantitatively and qualitatively alters the global gene expression profiles of ADC and SCC samples. For this, we utilized public lung transcriptome datasets available on The Cancer Genome Atlas (TCGA) site and analyzed the gene sets involved in the cellular processes that are suggested to be associated with SETDB1 function. We identified interesting molecular signatures associated with the roles of SETDB1 in lung cancer and hope that these findings facilitate the development of biologically relevant targeted therapies for particular lung cancer types.To investigate the effect of SETDB1 overexpression in non-small cell lung cancers (NSCLC), we utilized the genomic datasets of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) deposited in The Cancer Genome Atlas . All plots were generated under R-environment or by MS-EXCEL.SETDB1 gene copy number and the expression level, the copy-number alteration (CNA) data were download from cBioPortal, and TCGA RNA-seq HTseq-count datasets of LUAD  and LUSC carcinoma  were downloaded using “TCGAbiolinks”  and histology (LUAD or LUSC) to generate box plots for the SETDB1 gene CNA vs. the expression levels. Correlations between SETDB1 CNA and expression in ADC and SCC were calculated using “cor” function with “method = pearson.”To examine the relationship between the iolinks” . The RNAiolinks” , and theSETDB1 levels and the top 20% (n ≅ 100) and the bottom 20% (n ≅ 100) of them were arbitrarily chosen as SETDB1-high (SH) and SETDB1-low (SL) group, respectively, for transcriptomic analysis. In ADC samples, the mean expression levels of the selected SH and SL groups were 4,211.15  and 1,562.89  in normalized counts, respectively, while in SCC samples they were 3,691.52  and 1,468.37 , respectively. Regarding normal samples, we used all normal samples  provided in the TCGA ADC and SCC datasets weather they were paired with tumor samples or not. Of 108 samples, 59 were from the TCGA ADC expression datasets and 49 were from the SCC datasets, with 33 and 17 of them paired with the tumor samples.The tumor samples were ranked by For survival analysis, clinical data for TCGA LUAD (ADC) and LUSC (SCC) patients were downloaded using “GDCquery,” “GDCdowload,” and “GDCprepare_clinic” functions in “TCGAbiolinks,” and datasets for SETEDB1 high and low patients were extracted and labeled as “Top100” and “Bot100,” respectively. Survival analysis was performed, and result was plotted using “TCGAanalyze_survival” function in “TCGAbiolinks.”2. Cells were transfected with 4 μg of a CMV promoter-driven RFP-SETDB1 expression plasmid per well using Lipofectamine 3000 (Invitrogen) and were observed for RFP signal in fluorescence miscroscope immediately before harvest 48 h after transfection.The human NSCLC cell line A549 was maintained in RPMI 1640 medium  supplemented with 10% heat-inactivated fetal bovine serum , 2 mM glutamine and antibiotics (100 U/ml penicillin and 100 μg/ml streptomycin) at 37°C in a humidified atmosphere of 5% COGAPDH gene were calculated using QuantStudio Design and Analysis Software (Thermo).Total RNAs were extracted from A549 cells and reverse transcription was performed by incubating 1 μg of DNase I−pretreated RNA with Superscript III enzyme (Invitrogen), 20 μM oligo−dT primers (Invitrogen), and 50 ng random hexamers (Invitrogen) at 50°C for 1 h. Five nanograms of the synthesized cDNAs was used for real−time quantitative PCR  with the specific primer sets for individual target genes . PCR wasSETDB1 expression dependent transcriptomic divergences in lung cancer samples, the TCGA LUAD, or LUSC samples were ordered based on the expression levels of SETDB1, and top-ranked and bottom-ranked 100 samples were selected; they were named as SH and SL groups, respectively. For t-distributed stochastic neighbor embedding (t-SNE), “Rtsne”1, a R wrapper for t-SNE, was used with “perplexity = 30” and “pca = true.” Then, transcriptomic data of SH and SL samples were extracted from the earlier DESeq2-normalized expression dataset for the differential expression analysis. For gene set enrichment analysis (GSEA), gene collections for “HALLMARK,” “KEGG pathways,” and “GO Biological Process” from MSigDB v7.0  were identified, and the results were visualized on volcano plots and heatmaps generated by “plot” and “ggplot” functions in R.In addition to the fGSEA that was generated based on group means, single sample GSEA (ssGSEA) was performed using gene set variation analysis (GSVA) , a R pacM-values for variance stabilization, and differentially methylated CpG sites (DMCs) were identified using “limma”  and annotated with genomic features  using “bedtools”  of TCGA of LUAD and LUSC samples were downloaded using “TCGAbiolinks,” and the pre-processed and scaled methylation beta-values for SH and SL samples were extracted. Next, beta-values were converted to edtools” . FinallySETDB1 transcript levels in the two most frequent histologic subtypes of NSCLC, ADC, and SCC. We inspected the genome and transcriptome data that are publicly available from the cBioPortal and The Cancer Genome Atlas (TCGA). The SETDB1 gene amplification was more frequent in ADC samples than in SCC ones  and normalized ES  that estimate the degree to which the genes in the terms are overrepresented at either the top or the bottom of the ranked list of genes, the selected terms mostly showed negative ES/NES values. It indicated that those genes in the designated collections were mostly underrepresented in the SH samples in both the ADC and SCC groups . OverallCDH2/NCAD, were significantly underrepresented in the SH samples in both ADCs and SCCs  expression  in the SH samples in the ADCs  were largely underrepresented in the SH samples , 3E. Thithe ADCs . The undp < 0.002) in the ADC-SH samples with positive ES values: “RNA_METABOLIC_PROCESS,” “PRE_MIRNA_PROCESSING,” “PRODUCTION_OF_SMALL_ RNA_INVOLVED_IN_RNA_INTERFERENCE/GENE_SILENC ING_BY_RNA,” “RNA/NCRNA_EXPORT_FROM_NUCLEUS,” etc.  in association with ARGONAUTE-1 and -2 . The genS,” etc.  althoughSETDB1 level, we performed single sample GSEA using gene set variation analysis (GSVA), which assesses separate enrichment scores for each pairing of a sample and gene set (independent of phenotype labeling) to find out to what extent the genes in a certain gene set are coordinately upregulated or downregulated within a sample (FDR < 1 × 10–5) showed that, consistent with the result in To further test if the ADC and SCC samples could be divided by a sample . The GSVBecause SETDB1 is one of epigenetic players, or epidrivers, that function as a writer, reader, and/or eraser of chromatin marks , the altp < 0.02) between the SH and SL samples in both ADCs and SCCs, and the “HISTONE_UBIQUITINATION” genes differed in the ADCs  between the SH and SL samples. The genes in the H3K4, H3K9, H3K36, and H4K20 methylation categories significantly differed (p < 0.01) in the ADCs, whereas only H3K4 and H3K9 category genes differed in the SCCs (As shown in the ADCs . We furtthe SCCs . The incn = 154) encoding effector enzymes and mediators that directly participate in the modification of nucleosomes  were mostly of lower methylation in the SH samples, whereas they were of higher methylation in the SCCs , as illustrated in The antithetical pattern of the methylation change was also observed around the CpG island (CGI) regions. In the ADCs, the differentially methylated CpGs identified between the SH and SL samples  and lung squamou-cell carcinoma  were downloaded from Genomic Data Commons Data Portal , and copy number alteration data were obtained from cBioPortal .Gene expression datasets for TCGA LUAD (Y-KK: conception, design, and administrative support. BM: collection and assembly of data. Y-KK and BM: data analysis and interpretation. Both authors wrote and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


P = 0.005). Furthermore, the overall survival of GCB patients defined by the DLBCL-COO assay was significantly superior to that of ABC patients (P = 0.023). This effect was not seen when the tumors were classified by the IHC algorithm. The DLBCL-COO assay provides flexibility and accuracy in DLBCL subtype characterization. These findings demonstrated that the DLBCL-COO assay might serve as a useful tool for guiding prognostic and therapeutic options for DLBCL patients.The well-established cell-of-origin (COO) algorithm categorizes diffuse large B-cell lymphoma (DLBCL) into activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subgroups through gene expression profiling. We aimed to develop and validate a qPCR-based gene expression assay to determine the COO subgroups of DLBCL with formalin-fixed paraffin-embedded (FFPE) tissue. We first established a DLBCL transcriptome database of 1,016 samples retrieved from three published datasets . With this database, we identified a qPCR-based 32-gene expression signature (DLBCL-COO assay) that is significantly associated with the COO subgroups. The DLBCL-COO assay was further validated in a cohort of 160 Chinese DLBCL patients. Biopsy samples from DLBCL patients with paired FFPE and fresh frozen tissue were collected to assign COO subtypes based on the immunohistochemistry (IHC) algorithm , DLBCL-COO assay, and global gene expression profiling with RNA-seq. For 111 paired FFPE and fresh DLBCL samples, the concordance between the IHC, qPCR, and RNA-seq methods was 77.5% and 91.9%, respectively. The DLBCL-COO assay demonstrated a significantly superior concordance of COO determination with the “gold standard” RNA-seq compared with the IHC assignment with Han's algorithm ( Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of malignant lymphomas, accounting for more than 40% of newly diagnosed cases. Although DLBCL is potentially curable with standard treatment, there is an urgent need for new therapies since most refractory or relapsed patients will eventually die from the disease. DLBCL has been recognized as a group of heterogeneous diseases with diverse genetic features and variable clinical outcomes. Almost two decades ago, Alizadeh et al.  performede novo and refractory/relapsed DLBCL patients  that allows differentiation between the GCB and ABC DLBCL subtypes in FFPE specimens using a quantitative reverse transcription polymerase chain reaction (qPCR) platform and evaluated the DLBCL-COO assay against RNA-seq and IHC assays. We further discussed its potential application in routine clinical practice as well as the clinical development of novel therapies for DLBCL patients.The DLBCL gene expression datasets with confirmed COO subtypes were collected from a public data repository, the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, and curated to form a comprehensive DLBCL transcriptome database. The gene expression datasets retrieved from three GEO series  were mainly conducted on two different Affymetrix oligonucleotide microarray platforms, including the GeneChip Human Genome U133A Array and the U133 Plus 2.0 Array. Detailed descriptions of the specimen characteristics and clinical features are provided in the original studies.www.bioconductor.org). The single-channel array normalization (SCAN) approach from the SCAN-UPC package was used to process the Affymetrix microarray data   , 14. Proion 3.0) .The DLBCL-COO assay was developed on the Applied Biosystems 7500 Real-Time PCR system , targeting 32 candidate markers and three housekeeping genes identified with microarray analysis. To support clinical applications using FFPE samples with poor RNA quality, primers were designed to amplify short template mRNA regions of exon-spanning junctions. In addition, the TaqMan MGB probes incorporate a 5′ fluorescent reporter dye and a 3′ nonfluorescent quencher, which offers the advantage of lower background signal, resulting in better precision in quantitation.de novo DLBCL, not otherwise specified .The study was approved by the ethical committee of Fudan University Shanghai Cancer Center . Specimens between June 1, 2012, and December 19, 2018, that were maintained at the Department of Pathology, Fudan University Shanghai Cancer Center, were retrospectively archived in the current study. All cases were reviewed by two independent pathologists (X-NJ and X-QL) in compliance with WHO classification and were histologically confirmed as The following antibodies  were applied on a BenchMark XT automated immunostainer  with Cell Conditioning 1 heat retrieval solution : anti-CD10, anti-BCL6, and anti-MUM1. For all staining procedures, tonsils with reactive hyperplasia served as external controls and reactive lymphocytes as internal controls. The cut-off value for tumor positivity was set at 30% of tumor cell staining for CD10, BCL6, and MUM1. Cases were designated as GCB or non-GCB using the algorithm specified by Hans et al. . The morTotal RNA was isolated from FFPE tissue and fresh tissue using the RecoverAll Total Nucleic Acid Isolation Kit  per the manufacturer's guidelines. The concentration of total RNA was quantified by a Qubit 3.0 Fluorometer , while RNA integrity and quality were further appraised using agarose gel electrophoresis. For each sample, reverse transcription was performed on isolated total RNA using the High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor . The PCR program consisted of an initiation step at 95°C for 10 min, followed by 40 cycles at 95°C for 15 s and 60°C for 1 min. All measurements were taken in triplicate. The melting curves of each measurement were checked; only the coordinate results were included in the subsequent analysis. Three genes  that have been reported to be consistently expressed in DLBCL cells were selected as housekeeping genes. First, qPCR results of housekeeping genes with various sample storage duration and RNA quality were investigated, and then, the average Ct value of each target gene minus the mean of three housekeeping genes was calculated as ΔCt. The –ΔCt value of each gene was applied for downstream analysis.https://github.com/DeplanckeLab/BRB-seqTools). The GEP-based classification method was performed to determine the COO molecular subtype of each specimen as described in Wright et al.  using 1 μg of RNA extracted from fresh tumor tissue according to the manufacturer's instructions. The raw sequencing data were preprocessed using the BRB-SeqTools suite .For comparison with the Hans-based IHC method, all COO subtypes of samples from the GEP methods were categorized as either “GCB” or “non-GCB.” All GCB predictions remained GCB, and any “ABC” or “UNC” subtype predictions from the RNA sequencing and qPCR assays were converted to the “non-GCB” subtype. The concordance between any pair of assays was calculated using only the total number of samples that could be called by both of those assays. The overall percent agreement and asymptotic 95% confidence intervals (CIs) are presented. To determine the positive percent agreement (PPA) and negative percent agreement (NPA), the global GEP-based subtyping method served as a standard reference in each comparison. Overall survival (OS) was defined as the time from diagnosis until death or the last follow-up date. Kaplan–Meier survival curves were constructed for OS analysis. A value of in silico test sets to evaluate the classification performance: one was composed of the gene expression profiles of 215 fresh tumor samples , and the other was composed of the gene expression profiles of 451 FFPE samples . Third, the developed qPCR assay was clinically validated against the RNA-seq and IHC assays on paired fresh and FFPE samples of de novo DLBCL patients treated at Fudan University Shanghai Cancer Center . To create a DLBCL transcriptome database for COO subtype classification, we performed a systematic search of major biological data repositories  to collect gene expression data sets of DLBCL samples with a confirmed COO subtype status. Overall, we accumulated the gene expression profiles of 1,016 samples to form a comprehensive DLBCL transcriptome database. We further adopted a training–testing–validation approach to identify and validate a reliable gene expression signature in this study. First, the gene expression profiles of 167 ABC samples and 183 GCB samples were retrieved from the database and curated to form a training set. Second, two independent cohorts were used as BCL6, UBC, AICDA, LMO2, UCHL1, and MME  was prepared for downstream bioinformatics analyses. Extracting a subset of informative genes from high-dimension genomic data is a critical step for gene expression signature identification. Here, we deployed the RFE-SVM algorithm with the linear SVM classification model and the parameter C equal to 1. The algorithm identified a compact panel of 32 genes that are significantly associated with the two molecular subtypes. As listed in  and MME .The classification model comprising 32 subtype-specific genes was established using the entire training set and then applied to Test Set 1, which was composed of 71 ABC and 144 GCB fresh frozen samples. With the 32-gene expression signature, 69 samples were classified as ABC and 146 as GCB. The overall agreement between the 32-gene expression signature classification and the reference diagnosis reached 97.2% . The PPA was 98.6% , and the NPA was 94.4% , considering GCB as positive . It was P = 0.005). Additionally, the PPA and NPA of the DLBCL-COO assay assigning GCB/non-GCB were 88.2%  and 93.5% , respectively.A total of 160 DLBCL patients with confirmed COO subtypes based on IHC assignment were enrolled in the current study. Han's algorithm assigned 60 cases (37.5%) as GCB and 100 cases (62.5%) as non-GCB. One hundred fifty-nine of 160 FFPE specimens met all criteria and were successfully assayed by the DLBCL-COO assay. We first evaluated the hierarchical clustering of the 32 genes and 159 samples based on the qPCR data. Complete linkage hierarchical clustering analysis was performed where the metric of similarity was Pearson's correlation between the 32-gene expression profiles of the samples. As shown in P = 0.091). However, the OS of GCB patients defined by the DLBCL-COO assay was significantly superior to that of ABC patients (P = 0.023). The patients assigned as unclassified DLBCL had an intermediate OS .The studies involving human participants were reviewed and approved by the Clinical Research Ethics Committee of Fudan University Shanghai Cancer Center. The patients/participants provided their written informed consent to participate in this study.W-HY, X-NJ, W-GW, and X-QL designed the study. W-HY, X-NJ, W-GW, Y-FS, Y-XW, and Z-ZL performed the experiments. W-HY, Y-FS, and Q-HX analyzed all data. W-HY, X-NJ, W-GW, and Y-FS wrote the initial manuscript draft. Q-HX, X-YZ, J-NC, X-NH, and X-QL critically revised the manuscript and gave valuable insight to the study concept. All authors revised the manuscript and read and approved the final manuscript.Y-FS, Y-XW, Z-ZL and Q-HX are employed by Canhelp Genomics Co., Ltd. The authors declare that this study received funding from Canhelp Genomics Co., Ltd. The funder had the following involvement with the study: data collection and analysis, and preparation of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The basal subtype was associated with worse overall survival in patients receiving no neoadjuvant chemotherapy (NAC), but better overall survival in patients receiving NAC in two clinical trials. Each of the subtypes could be further divided into chr9 p21.3 normal or loss subgroup. The patients with low expression of MTAP/CDKN2A/2B (indicative of chr9 p21.3 loss) had a significantly lower response rate to anti-PD-L1 immunotherapy and worse survival than the patients with high expression of MTAP/CDKN2A/2B. This integrative analysis reveals intrinsic MIBC subtypes and biomarkers with prognostic value for the frontline therapies.Only a subgroup of patients with muscle-invasive bladder cancer (MIBC) are responders toward cisplatin-based chemotherapy and PD-L1 blockade immunotherapy. There is a clinical need to identify MIBC molecular subtypes and biomarkers for patient stratification toward the therapies. Here, we performed an integrative clustering analysis of 388 MIBC samples with multi-omics data and identified basal and luminal/differentiated integrative subtypes and derived a 42 gene panel for classification of MIBC. Using nine additional gene expression data  using multi-omics data from The Cancer Genome Atlas. Using a gene panel for classification of MIBC derived from gene expression data, they find that the basal subtype is associated with worse survival in patients receiving no neoadjuvant chemotherapy (NAC), but better survival in patients receiving cisplatin-based NAC and further identify genes associated with response to PD-L1 blockade immunotherapy, suggesting potential clinical use of these genes’ expression signature. Urothelial cell carcinoma is the predominant histological type, which is often classified as non-muscle invasive bladder cancer (NMIBC), or muscle invasive bladder cancer (MIBC) depending on if tumor has invaded into the muscularis propria. Although only about 25% of newly diagnosed patients present with muscle invasive disease, MIBC has accounted for the majority of bladder cancer mortality2. In the past several decades, no major progress was made in the treatment of MIBC and the standard-of-care was limited to chemotherapy and radical cystectomy5. Recently, immune checkpoint inhibitors were used in clinical trials to treat metastatic bladder cancer, representing a new therapeutic direction10.Bladder cancer is the most common urinary tract malignancy with approximately 549,000 new cases and 200,000 deaths worldwide in 201811. High throughput molecular profiling studies have provided a great opportunity to study the heterogeneity of MIBC. In an effort to identify subgroups of patients that may benefit from systemic therapies, researchers classified MIBC into different molecular subtypes based on various gene expression (GE) signatures. For instance, Volkmer et al. identified a subgroup of MIBC patients who were characterized by high expression of cytokeratins  and cell surface receptors  and were associated with worse overall survival12. For this reason, this type of tumors was named as basal subtype13. Damrauer et al. and Rebouissou et al. classified MIBC into basal-like and non-basal-like  subtypes, while Choi et al. classified MIBC into basal, luminal, and p-53 like subtypes16. Recently, Mo et al. developed a tumor differentiation classifier that stratified MIBC patients into basal and differentiated subtypes, which were consistent in predicting patient survival in multiple MIBC cohorts17. Additionally, other research groups including The Cancer Genome Atlas (TCGA) research network and the bladder cancer molecular taxonomy group further classified MIBC into 4 or more subtypes22. However, it was generally agreed that MIBC could be basically classified into two major subtypes: basal/squamous-like (BASQ) that shows basal/stem cell features and differentiated/luminal that shows differentiated urothelial cell features23.MIBC is a heterogeneous disease with poor survival 20. However, these multi-omics data have not been fully integrated to identify MIBC subtypes. In a traditional clustering analysis, a certain number of omics features are selected for platform-specific analysis, which usually include informative and non-informative features. As a result, the clustering outcomes could vary by the selected features and clustering algorithms. To integrate multi-platform clustering results, a second clustering analysis  based on the platform-specific results is usually performed24. This step-wise clustering approach does not fully take advantage of the inherent structure of multi-omics data and thus may miss the opportunity to reveal the driving factors that determine the tumor subtypes. We hypothesized that a truly integrative clustering (iCluster) analysis of MIBC multi-omics data will be beneficial to discover the inherent driving factors that are essential for tumor classification. The iCluster methods are powerful tools that have been widely used by TCGA and other research groups to characterize a variety of cancers28. The major characteristics of the iCluster methods are that they can incorporate multi-omics data into a statistical model to perform sample clustering, and at the same time identify driver (informative) omics features from passenger (non-informative) features, which overcome the limits of the platform-specific and step-wise clustering approach24. Therefore, in this study, we aimed to use the state-of-the-art iClusterBayes method to identify MIBC integrative subtypes (iSubtypes) and subtype-specific biomarkers and to evaluate their clinical relevance28.TCGA generated multi-omics data including somatic mutation, GE, DNA copy number, and methylation for over 400 MIBC samples, which were a great resource to study MIBC subtypes17. The top mutated genes that contributed to defining the subtypes included TP53, KDM6A, RB1, FGFR3, TBC1D12, ELF3, and NFE2L2 , RB1 , and NFE2L2 . In contrast, the iLuminal subtype was characterized by higher frequencies of mutations in KDM6A , FGFR3 , TBC1D12  and ELF3 . A region in chr9 p21.3 containing interferon alpha (IFNA) genes, MTAP and CDKN2A/2B was found to be the major contributor for sample clustering, which could be divided into copy number normal (9p21.3 N) and loss (9p21.3 L) regions  and RB1 , but a lower frequencies of mutations in FGFR3  and NFE2L2 . However, overall, the iSubtypes and their 9p21.3 L/N subgroups were not significantly associated with tumor mutation burden , luminal papillary (LumP), luminal nonspecified (LumNS), luminal unstable (LumU), stroma-rich, and neuroendocrine-like (NE-like). These subtypes were generally concordant with the two major basal and luminal subtypes reported by TCGA and other groups21. Mo et al. also reported two GE subtypes, namely basal and differentiated17. Interestingly, we found that iSubtypes and the tumor differentiation subtypes were highly concordant, with an overall concordance rate of 86% . Their survival curves , demonstrating an improvement of patient classification in terms of survival. The iSubtypes were significantly associated with pathologic stage , T stage (P = 0.0017), and gender (P = 0.027) . There was no significant difference in overall survival among the basal-squamous, luminal, luminal-infiltrated, and neuronal subtypes . Similarly, although there were 6 consensus subtypes, only LumP was significantly different from the other 5 subtypes . There was no significant difference in overall survival among the Basal-squamous, LumNS, LumU, NE-like, and Stroma-rich subtypes . There were 10 patients with a history of NAC in this cohort of patients in which 6 patients were clustered to the iBasal subtype and 4 patients were clustered to the iLuminal subtype , luminal-papillary (35%), luminal-infiltrated 19%), luminal (6%), and neuronal (5%)86% Fig. . Compare86% Fig. . The res9%, lumin17. In general, the basal markers had a higher expression in the basal subtypes, while the luminal markers had a higher expression in the luminal subtype  that studied the clinical activity of PD-L1 blockade with atezolizumab in patients with locally advanced and metastatic urothelial cancer6. The RNA-seq data were generated from pre-treatment tumor samples of the patients. Using the TCGA data as the training set and the IMvigor210 data as the testing set, we classified the IMvigor210 samples into 2 subtypes using the k-nearest neighbor (KNN) method and the 18 TD genes and the 42 basal-luminal genes, respectively. The classification results of the IMvigor210 samples based on the 42 genes are shown in Fig. P = 0.041, Log-rank test). Remarkably, the 18 TD genes had similar power in classifying the patient samples into 2 subtypes with distinct survival .The iCluster analysis identified 42 classical basal-luminal markers as the subtype drivers, which included the 18 tumor differentiation (TD) genes Fig. 17. In geMTAP and CDKN2A/2B  and low expression (G3Low) groups  and in the basal  and luminal sub-cohorts , while the overall difference in overall survival was primarily driven by the basal and luminal subtypes themselves .As described previously, the basal and luminal iSubtypes could be further divided into copy number normal 9p21.3 N) and loss (9p21.3 L) regions that contain  N and losal Fig. , P = 0.2rts Fig. , P = 0.6P = 0.00084, Fisher exact test). The G3High group was also associated with a higher expression of PD-L1 , and better overall survival , compared to the G3Low group. Similar results were also observed in the basal and luminal sub-cohorts, respectively  subtype with the highest response rate was associated with the best overall survival  response rate of the PD-L1 blockade therapy of the G3High groups were at least 2-fold higher than the response rate of the G3Low group in the IMvigor210 cohort  on clinical outcomes and the impact of MIBC subtypes31. Using the TCGA GE data as the training data set, we classified the samples in the other 8 data sets into two subtypes based on the TD (18 genes) and basal-luminal (42 genes) signature, respectively. Figure P = 0.013, Log-rank test), which was similar to the results observed in the TCGA and IMvigor210 cohorts and consistent with other published results16. In contrast, the basal subtype defined by 42 basal-luminal GE signature was associated with better overall survival  in the two clinical trial cohorts, implying basal subtype responded better to the NAC than the luminal subtype33. Similarly, the TD GE signature also had excellent discriminating power in classifying MIBC samples into subtypes with distinct survival in the 6 retrospective cohorts  and the 2 clinical trial cohorts .To further test the prognostic power of the basal and luminal signature revealed in the TCGA data, we collected 8 additional microarray GE data sets. Six microarray data sets were from retrospective studies in which the detailed information for chemotherapy was not publicly availableIn an effort to identify the pathways that were altered between the basal and luminal subtypes, we performed GSEA on the well-established BIOCARTA and KEGG pathways using the TCGA data. Figure (TILs) by performing GSEA on immune-cell-specific gene sets34. The GSEA results showed that the innate immune cells including macrophages  and neutrophils (FDR = 0.0014) and adaptive immune cells including T cells (FDR = 0.014), T helper 1 (Th1) cells (FDR < 0.0001), Th2 cells (FDR = 0.05) and Cytotoxic cells (FDR = 0.05) were significantly enriched in the basal subtypes  and can be used to measure the enrichment of a set of genes among the top of a ranked GE profile. Overall, the basal subtype had higher immune score  and microenvironment score (P = 3.12E-7) than the luminal subtype , CDKN2A encodes protien p14ARF, which can bind to and sequester MDM2 (a E3 ubiquitin ligase), leading to blocking MDM2-mediated degradation of p5351. Therefore, CDKN2A/2B encodes tumor suppressors capable of inducing cell cycle arrest in G1 and G2 phases. The chr9 p21.3 deletion has been reported in bladder cancer cell lines and tissue samples as well as other cancers including glioma, leukemias, lung cancer, and mesotheliomas54.Both the basal and luminal subtypes could be further divided into chr9 p21.3 N  and chr9 p21.3 L (loss) subgroups Fig. . Chr9 p2MTAP/CDKN2A/2B low expression  groups had significantly lower anti-PD-L1 response rates and worse survival than the G3High (indicative of chr9 p21.3 N) groups. Interestingly, loss of chr9 p21.3 or low expression of the genes in the region was associated with worse survival in patients with NMIBC, melanoma, mesotheliomas, glioma, and oral squamous cell carcinoma58, which was consistent with our findings in patients with MIBC. Although PD-L1 had a higher expression in the basal subtype than that in the luminal subtype . In the Lund subtypes, the basal with the highest median PD-L1 expression did not have the highest anti-PD-L1 response rate , while the luminal subtype was characterized by relatively high expression of luminal markers of terminally differentiated urothelial umbrella cells  , or indirectly by altering the proportions and phenotypes of tumor-associated immune cell subsets61.The clustering of the TCGA samples to the two major basal and luminal subtypes were primarily driven by the GE and methylation patterns, which were negatively correlated. Currently, GE data are the most popular omics data type and most of the bladder cancer subtypes are defined by GE signatures. In order to make our findings useful in practice, we derived a 42-gene panel including the 18 TD genes as a surrogate of the identified 2655 driver genes Fig. 3) Figs. . Using t Figs. . 3) Figs. . Interes62. It is unclear if these findings are related to previous functional studies showing basal cancer stem cell repopulation is a contributing driver to chemoresistance63. Future studies will be essential to shed light on the current controversies.In summary, we identified 2 major MIBC subtypes with distinct landscapes across multi-omics levels. The basal subtype was associated with worse survival in the non-NAC patients of the TCGA cohort, but better survival in the NAC patients of the 2 clinical trial cohorts. Each of the 2 major subtypes could be further divided into 2 more subgroups according to the copy number status of chr9 p21.3. Patients with copy number normal of chr9 p21.3 tended to have a higher response rate to PD-L1 blockade therapy and better overall survival. Considering the subtype-specific responses to frontline chemotherapy and immunotherapy, it may be worth exploring the combination therapy in MIBC. Our study has some limitations. First, the sample size of the two clinical trial cohorts was relatively small. More clinical trials with larger sample sizes are necessary to confirm the findings. Second, the history of NAC was known for the patients in the TCGA cohort, but that information was not publicly available for patients in the other 6 retrospective cohorts, which might consist of NAC and non-NAC patients. Third, bulk RNA-seq and microarray GE data are confounded by signals from a mixture of cell populations, making it necessary to collaborate these RNA-based findings with multiplex immunohistochemistry to investigate tumor cell intrinsic changes and its crosstalk with the immune and stromal microenvironments that dictate therapy response. Finally, we are aware that a recent study using the consensus classification method in a different patient cohort revealed basal/squamous tumors exhibited a poor response to chemotherapyhttp://firebrowse.org/. Among the 412 TCGA patient samples, 388 samples with complete somatic mutation, DNA copy number, methylation, and RNA-seq GE data were used for iCluster analysis. Among the 388 samples, 378 samples were collected from patients who received no NAC and 10 samples were collected from patients who received NAC. IMvigor210 RNA-seq GE data (n = 348) were from a large phase 2 trial investigating the clinical activity of PD-L1 blockade therapy using atezolizumab in locally advanced and metastatic urothelial carcinoma6 and made available by Mariathasan et al.29. Eight microarray data sets were obtained from ArrayExpress64 including CIT  14 and from GEO65 including MDA1  16, MDA2 16, CNU  30, MSK  32, LU  19, MDA MVAC 16 and MDA MVAC + B 31. In the MDA MVAC study, patients were enrolled for a phase III clinical trial and treated with MVAC 66. In the MVAC + B study, patients were treated with dose-dense MVAC plus bevacizumab (MVAC + B) in a phase II clinical trial31. The other 6 studies were retrospective and the detailed information for chemotherapy was not publicly available. The collection and molecular profiling analysis of the samples were approved by individual Institutional Review Board and informed consent was obtained from each subject as part of previously published studies.The MIBC multi-omics data were generated by TCGA and the level-3 data were downloaded from 28. To perform the analysis, we processed the MIBC multi-omics data sets to form 4 data matrixes with columns corresponding to the common samples (n = 388) and rows corresponding to omics features. A flowchart summarizing the process was shown in Supplementary Fig. 27. For the methylation data, we used the beta values that had minimum correlation with corresponding mRNA expression and selected the top 25% (4226) most variable probes for iCluster analysis. For the mRNA expression data, we used the top 25% (5134) most variable genes. In order to fit the model better, we performed logit transformation of the methylation beta values and log2 transformation of the mRNA-seq normalized count values. To find an optimal number of clusters, we tested the cluster number parameter K from 1 to 6. For each K, we ran 36,000 Markov chain Monte Carlo (MCMC) iterations for estimation of model parameters, of which the first 18,000 were discarded as burn-in. After examining the deviance ratio, Bayesian information criterion and heatmap for each possible number of clusters, we found that a 2-cluster solution was optimal  was chosen so that the cross-validation error was minimum in the TCGA GE data. For the microarray GE data, when a gene’s expression was measured by multiple probes, we used the one with the largest variance for the classification analysis. To make the data sets comparable for classification analysis, the GE values were standardized across genes for each sample. Pathway and gene set enrichment analysis were performed using GSEA 3.0 (https://www.gsea-msigdb.org/gsea). Gene ontology term enrichment analysis was performed using the DAVID bioinformatics tools (v. 6.7) (https://david.ncifcrf.gov). Cell type enrichment analysis was performed using xCell (2017) (https://xcell.ucsf.edu/).To examine if the GE signature  identified by the iCluster analysis had prognostic value, we performed classification analysis using the k-nearest neighbor method in which the TCGA GE data were used as the training data set, and the other GE data were used as the testing data sets. The number of https://www.r-project.org). P values were two-sided and P-value < 0.05 was considered statistically significant. If multiple comparisons were involved, p-values were adjusted using Benjamini–Hochberg method.Subtype-specific survival was estimated by Kaplan–Meier method and compared by log-rank test. Fisher’s exact test was used to evaluate if somatic mutations were associated with subtypes. Two sample t-test (or Wilcoxon rank-sum test) was used to compare two groups of samples with continuous measurement, and Analysis of Variance (ANOVA) was used for comparison of three or more groups. All the statistical analyses were performed using R 3.6.1 (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


The resulting high dimensional object is amenable to unsupervised screening of structural alterations across RNA-seq cohorts with nearly no assumption on the mutational mechanisms underlying abnormalities. This enables SCISSOR to independently recapture known variants such as splice site mutations in tumor suppressor genes as well as novel variants that are previously unrecognized or difficult to identify by any existing methods including recurrent alternate transcription start sites and recurrent complex deletions in 3′ UTRs.High-throughput sequencing protocols such as RNA-seq have made it possible to interrogate the sequence, structure and abundance of RNA transcripts at higher resolution than previous microarray and other molecular techniques. While many computational tools have been proposed for identifying mRNA variation through differential splicing/alternative exon usage, challenges in its analysis remain. Here, we propose a framework for unbiased and robust discovery of aberrant RNA transcript structures using short read sequencing data based on shape changes in an RNA-seq coverage profile. Shape changes in selecting sample outliers in RNA-seq, SCISSOR, is a series of procedures for transforming and normalizing base-level RNA sequencing coverage data in a transcript independent manner, followed by a statistical framework for its analysis ( Many computational tools identify mRNA variations by analyzing the transformed RNA-seq data such as collapsed reads. Here, the authors report a computational method which uses shape changes in the RNA-seq coverage profile to discover changes in mRNA expression and alternative splicing. For example, tumor-suppressor genes and oncogenes can lose or gain function through aberrant splicing, gene fusions, duplication, short insertions, and deletions (indels), or overexpression10. These alterations are emerging as relevant targets of therapy, and thus the systematic discovery of such alterations is critical12. Many computational methods can identify mRNA aberrations from RNA-seq, but most use a limited subset of the total RNA-seq data available. For example, isoform detection methods that emphasize known transcripts such as DEXSeq13 and SpliceTrap14 collapse reads aligning to exons to a single value without incorporation of spliced reads. Alternatively, transcript agnostic methods such as MISO15, rMATS16, and DiffSplice17 generally ignore non-junction-spanning reads, and analysis is restricted to pre-specified events of interest such as intron retention or exon skipping18. In either case, the transformed RNA data object is a greatly compressed representation of the true underlying data, and this can obscure various types of RNA variation. Therefore, there is an unmet need for unbiased methods that work on a less-compressed—or base resolution—representation of the transcriptome and thus could have greater power to reveal novel biological insights20.Many human genes differ in function through various expression changes in mRNA product21. We then identified novel aberrations missed by previous studies on the same data set including alternate splice isoforms, fusions, and intragenic deletions. Finally, we applied SCISSOR genome-wide to identify a novel set of genes with strong evidence of aberrant structure in HNSC.Here we present SCISSOR, an approach for systematic discovery of changes in mRNA expression including alternative splicing, intron retention, de novo splice sites, intra-/intergenic deletions, and alternative transcription start/termination (ATS/ATT) sites. RNA changes may be somatic, as the result of driver or passenger mutations, germline variants, or non-genetic events resulting from epigenetic regulation of alternate isoforms. In contrast to existing methods, SCISSOR uses base-resolution read coverage data that provide a rich and comprehensive landscape beyond gene/exon expression values or splice junctions. SCISSOR aims to detect structural variation, or differential coverage patterns, across RNA-seq cohorts without any underlying assumption of the mechanism driving the coverage variation. This enables us to reduce our dependency upon known gene models and increase our potential to confidently identify otherwise obscured genetic aberrations. As a proof of principle, we used SCISSOR on a cohort of 522 TCGA head and neck squamous cell carcinomas (HNSC) to identify known abnormalities in cancer-related genes22. The method allows for transcript-independent analysis based on genome alignments, although for the sake of visualization, convenience, and interpretation in the current example we limit this analysis primarily to annotated regions of the genome and exclude most intronic bases (see “Methods” section). Base-level read-depth represents an easily interpreted view of coverage shape of genes, with the y axis proportional to per-base expression and the x axis showing the genomic position . We define a global shape change (GSC) as one that appears in a wide range within a gene, including altered exons or introns, intragenic deletions, and fusions. The directions supporting disjoint exonic or intronic regions are considered as a set of orthogonal bases of a low-rank space that will be searched by the modified projection outlyingness method. As a result, the MOD of an outlier detected by the global shape change detection is established by a linear combination of the bases (or a basis itself), which enables the interpretation of the abnormality and the automatic characterization of its type  and previously unrecognized (18%) genetic aberrations in TP53, we identified 11 samples that were not documented in the TCGA mutation calls. In these 11 samples, we detected intron retention and skipping of multiple exons . This supports a potential impact of intronic variants (>2 bp) on abnormal splicing, which has been relatively underappreciated compared to SNVs in exons and canonical splice sites25, and thus shows the capability of our shape-based analysis to assess the functional impact of variants outside of the canonical splice sites.In addition to shape variation associated with previously reported mutations at FAT1 and CDKN2A, respectively, including all nine splice site mutations called at CDKN2A and multiple known and novel fusion-like structural variants events in FAT1  . Accounting for ethnicity, there was no clear enrichment in the TCGA HNSC cohort. Functional characterization in the UTR variants is challenging, and as such, they are generally ignored or given low priority. Although the functional impact of this variant is speculative, we note that the deleted region includes a known enhancer element  Fig. . This waMTAP, a polyamine metabolism enzyme, located immediately adjacent to the commonly mutated CDKN2A  as the first objective evidence that this gene is specifically and recurrently targeted for inactivation in TCGA HNSC. We conclude that SCISSOR can augment copy number analysis in defining the driver targets of deletion events , which initiate from exon 8 preceded by ~300 bp of intron 7 , no supporting transcripts have been reported in a public database, and no recurrent mutations were found.As proof of another important usage of shape changes, we identified recurrent alternative transcription start/termination (ATS/ATT) in a number of genes. ATS/ATT has been reported as the principal drivers of isoform diversity and a novel mechanism of oncogene activation in preliminary reportsn 7 Fig. . AlthougIn summary, it is clear that SCISSOR identifies both novel driver genes and novel mechanisms of gene alterations not easily identifiable by any other single method.31, shared artifacts, such as global RNA degradation34, differential sample cellular composition, such as the proportion of infiltrating immune cells35, or some other novel mechanism. Starting with an unsupervised approach, we investigated potential non-random associations between altered genes across samples by Fisher’s exact test applied to the number of co-altered genes in every pair of the cohorts. Four subjects were identified with high significance by sharing alterations in a number of genes  and exon-level expression-based analysis (ELBA) such as percent-spliced-in and DEXSeq30, and compared their performance in identifying the canonical alterations of TP53 as well as in simulated data , contaminating stroma cells (likely observed in the current data set and the source of future reports), GC content , or RNA fragment length . Users of SCISSOR should consider experimental as well as biological factors when interpreting the results of SCISSOR. In this work, we observed that RNA degradation, if not addressed, will result in degraded samples producing large numbers of outlier genes. We addressed this by empirically removing samples with evidence of degradation. We observed some weaker evidence coordinated RNA shapes shared by samples across many genes in association with certain types of internal CpG islands. Whether or not this is a GC experimental artifact or a biologic process is unclear from the current data. For those samples with evidence of retained intron, we considered the possibility of DNA contamination as an explanation. DNA contamination as assessed by the 260/280 ratio was consistent with pure RNA. Additionally, we would expect that contaminating DNA would be evidenced as a general phenomenon across many introns at lower or consistent levels rather than as we see it in highly selected introns at high coverage in a very limited number of introns.In its current form, SCISSOR has tuning parameters including log normalization and a normality cutoff value which will likely require some optimization when a significantly different wet lab RNA-seq protocol is used. In addition, because the optimization performed in SCISSOR is intensive and even infeasible for high-dimensional data, SCISSOR provides a good compromise with dimensionality and sparsity by implementing a two-step procedure. Possible issues from this are that the GSC detection algorithm loses information about orders of base pairs and that the LSC detection algorithm might give alternative results with different window sizes. For a future study, a complementary approach would be an all-in-one procedure that maximizes the outlyingness with respect to full-dimensional window directions by simultaneously searching for the optimum location and size of a window for each sample. Although our attention was focused on RNA-seq data, SCISSOR has great potential for future applications, including detection of non-coding RNA, DNA shape changes such as copy number alterations, and single-cell RNA-seq data. Such applications will likely explore some of the tuning parameters and other optimization in more detail.R each entry of which is the observed read count Rij mapped to each base position i in each sample j. The dimension of the matrix R is the number of base positions along the gene locus which is denoted by d. Therefore, the input pileup data R we consider at each gene is a d × n matrix in which d indicates the length of the gene, i.e., dimension, and n is the size of the cohort. In other words, each sample is a d-dimensional read count vector which can be thought of as a d-length trajectory with certain shape patterns . In many genes, we observed certain variations non-linearly associated with the overall expression (aj) in a gene-specific way  as a smooth function of an overall expression level satisfying a constraint g(1) = 1. Using this g(.), the second part of the model, mij, for remaining variation after adjusting for sample-specific expression levels, is further modeled by d-dimensional random vectors X ∼ Pd, where aj to account for a linear (g(aj)) effects of systematic variation that should be corrected. With this representation, SCISSOR provides a natural way to decouple outlier-associated signals from systematic variation by estimating and normalizing out the aj-associated terms from raw read counts . See Supplementary Note The raw coverage data often show heterogeneous variation typified by extreme skewness and different fluctuations within and between samples, implicating that highly expressed regions may dominate other biologically important variation. A typical remedy to adjust such heterogeneity of counts data is log-transformationg(.) to account for such remaining variations that may lead to biased results. In practice, we only observe Rij, and the other unknown elements μi, aj, and g(.) should be estimated. In brief, the estimation procedure follows as: (1) estimation of μi’s based on a trimmed mean at each base position; (2) estimation of aj’s by a robust linear model; (3) estimation of g(.) as a function of aj by fitting a smooth curve. See Supplementary Note xij as a main data object for the downstream shape change detection analysis by ruling the irrelevant terms out using the estimates  where K is the number of basis directions included. To find a more accurate cutoff value for the case beyond the normality assumption, we implemented a data-driven approach to search what degrees of freedom fit the p-value. Then, we declared global shape changes using the chi-squared distribution with the chosen df based on the pre-determined level α.We propose a two-step procedure each step of which is designed to reveal particular types of aberration. The first step aims to find global shape changes that involve exon- or intron-level changes such as large structural variations including fusion and large deletion. To identify global shape changes, we first approximate the data matrix using a set of components  each of which spans an exon or an intron. As projection outlyingness is independent of the coordinate system chosenWe now propose a second step procedure to deal with more challenging situations when outlying features are not distinguishable using the low-rank representation constructed by exon/intron bases. In RNA-seq data, it has been observed that shape variation associated with such weak signals often exhibit local changes in a limited region of base-level coverage. As these local shape changes generally remain in residuals, i.e., data after subtracting the low-rank representation, they are difficult to infer due to accumulated noise in residuals. Thus, the projection depth idea or other conventional outlier detection algorithm may be improper or infeasible.w, we can accurately capture challenging local shape changes while reducing the impact of an overwhelming number of dimensions. The consideration of window size was validated by the performance of the method  is defined as the direction that achieves the maximum in Eq. The identified MOD would be some exons, introns, or their combination for a global shape change whereas it would be some narrow area, rather than an entire exon or intron, for a local shape change. Thus, it naturally enables the interpretation of the identified abnormal events. Further, SCISSOR incorporates the MOD with the splice junction reads to determine whether the event is supported by splice reads or not, providing an automatic characterization of outlier types, e.g., (cryptic) exon skipping, (cryptic) intron retention, alternative transcript initiation/termination, or small deletion, etc 45, supporting the suitability of the proposed decay rates.To identify degraded samples, we obtained It has been observed that the genes where samples do not share an analogous pattern commonly have a considerable number of samples that appear to be unexpressed, and we call those genes “on/off” genes  v2.1 based on the December 2011 version of the UCSC Gene annotations. In contrast to many RNA-seq data analyses based on reads only at exonic regions with splice junctions, we include reads mapped to both exonic and intronic regions in order to include potential intronic aberration. Mutations were analyzed based on mutation annotation format (MAF) obtained from the TCGA Research Network21.A collection of 522 HNSC tumor samples were obtained from the TCGA Research NetworkAlthough SCISSOR does not require a gene model for most of its underlying assumptions, gene models greatly facilitate the biologic interpretation and visualization of results. The SCISSOR procedure, including normalization and outlier detection, can be applied directly to RNA aligned to the genome in a truly unbiased manner. However, such a procedure can produce results that are difficult to interpret as RNA is most easily described in the context of named genes, exons, introns, and splicing. As such, the SCISSOR procedure is applied in the context of named gene models to assist in the visualization and interpretation of the data. SCISSOR is unbiased in that space because it is not constrained by existing exon start or stop positions and incorporates intron space to allow detection of events outside exons such as run-on transcription. Additionally, although SCISSOR does not include splicing information in the statistical procedure, it does incorporate splices in the interpretation of shape changes once they are identified as described in the earlier section (Most outlying direction).L), it is fully included.If its length is less than or equal to a threshold (L, then the part of the intron is taken to be the union of two subsets of length If its length is greater than Gene models were modified to include a portion but not all of intronic regions to facilitate the common alterations that involve intron-exon boundaries. The omission of large portions of introns is reasonable because they complicate the visualization of RNA pileups and add little to biologic signal. The disadvantage of this decision is that important alternate splice sites and cryptic exons may be missed. To determine which parts of introns to be included in the model, a basic rule is that the total lengths of bases for all exons and all introns at a gene to be approximately equal for the current SCISSOR application. This helps to make variations of expression at exonic regions and intronic regions comparable. Further, the lengths of intron included between exons are determined by the following rule: for each intron between exons,L is chosen such that the difference between total lengths of exons and introns is minimized. As a result, the RNA-seq data as a data object for the analysis consist of exons and introns with equal weights.Here, the threshold Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using  Whole-genome expression data generated by microarray studies are a promising resource for human health risk assessment. Analysis of these data can provide insight into mechanisms of biological processes, enable prediction of adverse outcomes of chemical exposures, and support estimation of points of departure (PODs) for derivation of toxicity values (reviewed in ).in vivo setting have been shown to identify gene set -level PODs that approximate those identified using much more resource intensive guideline toxicity assessments [Genomic dose-response studies (GDRS) done in an essments ,3. In adessments .One approach to analysis of GDRS data is embodied in the BMDExpress software  which caAt each level of the analysis, parameter selection  is made that can dramatically impact the amount of information that is carried through the analysis. For the assessment discussed here we have used a set of parameters identified by the National Toxicology Program (NTP) .Before BMD modeling of microarray expression data can take place, the raw fluorescence signals must be processed. This processing, frequently referred to as “normalization”, mathematically transforms raw signals to expression estimates that are proportional to the amounts of corresponding transcripts in the profiled specimens. The need for normalization relates to a complex set of processes that can introduce non-biological variability, along with complex relationships between input quantities of mRNAs and signal intensities. These processes include, but are not limited to reverse transcription, labeling and hybridization on microarrays. Normalization of raw data from Affymetrix microarrays has been the focus of a remarkable amount of research due to the significant effect it can have when interpreting microarray data and the popularity of the Affymetrix platform. Numerous normalization methods are currently available, including MAS5.0.0, RMA, GCRMA, and PLIER. Each of these methods includes background adjustment , normalization and probe summarization steps . Due to in vivo genomic dose-response studies of crude 4-methylcyclohexanemethanol (crude MCHM) [In this article we demonstrate the influence of different normalizations of expression microarrays on the findings from 5-day de MCHM) , neat 4-de MCHM) , N,N-dimde MCHM)  and p-tode MCHM)  in liverde MCHM)  in kidnehttps://www.thermofisher.com/order/catalog/product/900505).All expression data used in this study were generated by profiling liver specimens of rats using the Affymetrix Rat Genome 230 2.0 Array that contains more than 31,000 probe sets analyzing transcripts from 15,575 annotated unique genes , or neat 4-methylcyclohexanemethanol (MCHM), and for kidneys of rats exposed orally to propylene glycol phenyl ether (PPH) were accessed from the Gene Expression Omnibus (GEO) as series GSE75655, GSE75657 and GSE75656, respectively. Hepatic transcriptomics data for F344/N rats exposed orally to N,N-dimethyl-p-toluidine (DMPT) or p-toluidine for 5 days  were accCrude MCHM represents a mixture of six components in addition to the major component 4-methylcyclohexanemethanol (MCHM) used as a coal cleaning liquid. Propylene glycol phenyl ether (PPH) is an industrial chemical used as a latex coalescent and a solvent for textile dyes. Toxicological significance of MCHM and PPH is associated with a recent spill and large scale contamination of drinking water in West Virginia . N,N-dimhttps://www.R-project.org) using the “GCRMA: Background Adjustment Using Sequence Information” R package version 2.52.0 (Bioconductor version 3.7). PLIER16 values were calculated from PLIER values by adding 16 to each signal intensity value as a simple variance-stabilizing transformation. Low-quality microarrays were identified by visual inspection of the Relative Log Expression (RLE) boxplots generated by Expression Console (Affymetrix). RLE values are calculated for each probeset as the ratio between expression of this probeset in a given microarray and the median expression of this probeset across all the arrays in the dataset [https://catalog.data.gov/organization/epa-gov).The raw fluorescence signals was processed using the following methods: MAS5.0.0 (Microarray Affymetrix Suite version 5.0), RMA , GCRMA  and PLIER (Probe Logarithmic Intensity Error Estimation) ,19. Raw  dataset . Low-quahttps://github.com/auerbachs/BMDExpress-2/releases) [Normalized data were imported into BMDExpress for Windows, version 2.20 build 0167 BETA (eleases) . For allDifferentially expressed probesets were detected using the Williams trend test . Benchmark response of 1 SD was used for each feature. Best models among linear, 2nd degree polynomial, Hill, power and exponential models (degrees 2–5) were selected based on the lowest AIC. For more details on parameters used in BMDExpress, see http://bioinformatics.psb.ugent.be/cgi-bin/liste/Venn/calculate_venn.htpl). All other plots were produced using TIBCO Spotfire Analyst version 7.8.0 . Agreement between probe-level BMD or BMDL values determined by different normalization methods was assessed by Pearson correlation using TIBCO Spotfire Analyst version 7.11.10 . Statistical significance of differences among probeset-level best model fit BMD and BMDL values for PPH determined by 7 normalization methods were tested using Kruskal-Wallis test and the differences were considered statistically significant for two-tail p-values<0.05. Most sensitive GO:BP gene sets were summarized for visualization using Revigo tool [Comparison of differentially expressed genes was visualized using Venn diagrams  and BMD values were determined from the remaining expression data Different normalization methods identified remarkably different numbers of differentially expressed probe sets (DEPSs). The highest numbers of DEPSs for most chemicals were identified by using the MAS5.0 and PLIER methods, while PLIER16 and PLIER16-noAcalls identified considerably less probe sets corresponding to differentially expressed genes . LikewisThe use of MAS5.0 and PLIER also produced the highest numbers of DEPSs with acceptable model fits , which would be therefore included in the gene set BMD analysis . ConsistComparison of overlaps among DEPSs with acceptable model fits across the 7 normalization methods was also performed for all data sets. In all chemical-organ sets, the percent of overlapping DEPSs with AMFs was quite small (<10%) when all normalizations were compared, suggesting that normalization is likely to have a large impact on the qualitative interpretation  of the toxicogenomic effects. To obtain a sense of how great the effect normalization has on the identification of DEPS with AMF, an “intersection” set for all normalizations per treatment-chemical pair was identified and used as a comparator for each normalization in a given chemical-treatment set . This anDistributions of probeset-level BMD and BMDL values showed differences across normalization methods . For exaSelection of different normalization methods influenced determination of lowest median gene set-level BMD (BEPOD) and BMDL (BEPOD/L) for different chemicals to a different extent. Within a gene set type for any one treatment, the BEPOD/L variation ranged from 1.1 fold  to up to 30.4 fold  due to different normalizations . The meaInfluence of normalization methods on the identity of the most sensitive GO Biological Processes and Reactome pathways varied across datasets . For insTo determine the effect of normalization on overall agreement of the BMD values, Pearson correlation between both probe set BMD values and median BMD values for GO:BP and Reactome pathways in each of the five experiments was performed. In nearly all cases, BMD values from probe sets (PS) or gene sets exhibited positive correlation, however the range of Pearson correlation coefficients (PCCs) varied from as high as 1, with average PCCs for any pair of normalizations mostly falling between 0.2 to 0.4 . In mostGene expression profiling has been identified as a promising method to address challenges in chemical risk assessment that have traditionally relied on data generated by time and resource consuming animal studies. This promise is especially true for short-term, in vivo, dose-response microarray studies that demonstrated potential to produce transcriptomics benchmark doses in good agreement with traditional toxicological studies ,2.Unlike RNA-seq, where methods for analysis are still evolving, gene expression microarrays are an established tool in transcriptomics studies. Considering the relative maturity of data analysis of expression microarrays, they will likely continue to play a substantial role in whole-genome expression studies particularly in cases where the data will be used for critical decision making  .Analysis of microarray expression data needs to address a non-biological variability introduced by sample preparation, labeling, hybridization, fluorescence reading and other technical issues through normalization of raw microarray data. Normalization procedures attempt to remove non-biological variability from data by exploiting and enforcing known or assumed invariances of the data using different approaches . Not surThe purpose of this study was to examine sensitivity of transcriptomics BMD modeling to some frequently used methods for microarray data normalization. Our results demonstrate that the use of different normalization methods produces considerably different lists of differentially expressed genes. Further, we found that the influence of microarray normalization methods on the results of transcriptomics BMD modeling is limited on some, but considerable on other datasets. We show that the fold differences between BMDL values determined from microarray data normalized by different methods can be as low as 1.1 -fold, but also as big as 30.4-fold. These differences are potentially noteworthy, because transcriptomic and apical BMD values can differ by a factor of ten, when transcriptomic values are determined using RMA normalization [Prior comparisons of microarray normalization methods have not produced a clear winner. While some investigators found GCRMA to perform as well as or better than other methods , others S1 Dataset(ZIP)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 Fig(PPTX)Click here for additional data file.S2 Fig(PPTX)Click here for additional data file.


Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency.After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.R2-score of a 10-fold CV of training data can reach 96.38%, and the R2-score of test data can reach 83.3%.Selecting features with around 800 genes for training, the These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions. Metastatic cancer is a metastatic malignant tumor that has been confirmed by biopsy, but the primary site cannot be found. The cancer cells from the primary site are brought into other organs by invading the lymph, blood, or other means . The cauIt is particularly important to find the primary focus in the clinical stage of cancer treatment. Only by finding the primary focus can the clinical cure rate of the patient be improved. Because the biological features often vary with the type of tumor tissue, we can make a pathological diagnosis based on the existing biological knowledge and established pathological methods. Due to the limited tissue and diagnostic staining of tumors and the influence of doctors’ professional level, there are still some loopholes and shortcomings in the thorough search at this stage .The transfer of cancer means that the tumor cells are taken to it from the primary site into the lymphatic vessel, the blood vessel, or other means to continue to grow to form the same type of tumor as the primary site. Common methods of transfer include lymphatic metastasis, vascular metastasis, and the like. About 50% of the lung cancer will have multiple bone metastasis sites, 28–33% of the liver metastasis, and 17–20% of the transfer of the kidney and the epinephrine. The auxiliary imaging examination is usually diagnosed by a biochemical indicator. In the liver metastases, the biochemical biopsy of the liver micro metastases may cause confusion due to the stability of the biochemical indicators; and in the imaging ultrasound examination, the lesions of 1–2 cm could be detected in random tests. The error of uncertain factors in a practical application will accumulate and magnify, resulting in diagnostic confusion.We aim to establish an automatic processing method to solve this problem. We selected data from gene expression profiles. By analyzing and processing the existing data, a relatively suitable machine learning model is obtained , and theIn essence, machine learning trains computers to simulate or realize human learning behavior to acquire new knowledge and skills and reorganize the existing knowledge structure to improve its own performance continuously. The application of medical treatment is also a process of comprehensive doctor diagnosis experience to treat patients. Many machine learning algorithms have been developed for classification problems. It can judge the unknown information by learning from the known information. By studying the existing tumor samples’ features, the computer has a certain decision-making ability to judge and evaluate the unknown cancer pathology directly.https://github.com/dmlc/xgboost and is widely used in bioinformatics. XGBoost based on tree boosting is a scalable end-to-end tree boosting system, which was first proposed by Data of 5,759 samples, each containing 20,501 gene characteristics, were downloaded from TCGA. After extracting effective information, we normalized the gene expression by the sum of all the sample gene expressions. We use oversampling with stable results to solve the problem of data imbalance, then we select and train the optimal model 10-fold cross-validation on TCGA data.We conduct retrospective testing on a GEO test set containing 42 samples covering five cancers. The trained model predicts the test data, and the results were compared with the true labels of the samples. The specific number of samples per cancer is shown in In the training set and the independent verification set, a part of the gene expression level was very low. We set the expression level threshold value as 0.00005, 0.00001, and 0.000001, respectively, for screening. After the intersection of the training set’s gene characteristics and the independent verification set, the following feature selection was conducted.k genes.We choose the Chi-Square test and Random Forest in the filtering method for feature selection. The Chi-Square calculates the correlation of qualitative independent variables to qualitative dependent variables. First, we take each gene as an independent hypothesis and then calculate the degree of deviation D between the observed value and the theoretical value. If the deviation is small enough, accept the null hypothesis; otherwise, reject the null hypothesis, and accept the alternative hypothesis. Therefore, the larger the deviation value D, the greater the deviation from the original hypothesis. That is, the more relevant it is, the better the selection process becomes at calculating the deviation value D of each gene and the type of cancer, and to order them from large to small, and to take the first The application of random forest in feature selection needs to calculate the feature importance. The specific steps are as follows: First, we calculate each feature’s importance and sort it in descending order. After that, we determine the proportion to be eliminated and get a new feature set by eliminating the corresponding proportion of features according to their importance. Repeat the process with the new feature set until there are m features left, which is the preset value. Finally, we select the feature set with the lowest out-of-bag error rate according to each feature set obtained in the above process and the corresponding out-of-bag error rate of the feature set.k-trees after training, we need to predict the score of a sample. In fact, according to the characteristics of this sample, each tree will fall to a corresponding leaf node, and each leaf node corresponds to a score. It is necessary to add up the scores corresponding to each tree to be the predicted value of the sample. XGBoost is based on gradient tree boosting. Unlike traditional trees, which only do the first-order Taylor expansion, XGBoost performs the second-order Taylor expansion, which realizes the parallel computation . It can We fine-tuned three hyperparameters within the 10-fold cross-validation. The parameter “n estimators” is the number of trees to be used in the forest. The parameter “max depth” is the deepest depth of all trees. The parameter “min child weight parameter” in XGBoost is the minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instances weighing less than the min child weight, the building process will give up further partitioning. This parameter is used to avoid overfitting. When its value is large, the model can be prevented from learning from outliers. But if this value is too high, it will cause under-fitting. The max depth is also used to avoid overfitting. The greater the max depth, the more outliers the model will learn.R2 score as an indicator of the evaluation model. At the same time, the test results are output, which included the R2 score, precision, recall rate, and the F1 score of each cancer calculation result shown in Use the R2 score the problem that MSE (Mean Absolute Error), RMSE (Root Mean Squared Error), and MAE (Mean Absolute Error) cannot solve when dimensions are different, and it is difficult to measure the effectiveness of the model. R2 score = 1, reaches the maximum value, and then MSE as the molecule is 0, which means that the predicted value and the true value in the sample are the same, without any error. In other words, the model that has been established perfectly fits all the real data, which is the model with the best effect and where the R2 score value reaches the maximum. The model is usually not so perfect; there are always errors; when the error is small, the numerator is less than the denominator; when the model tends to 1, it is still a good model. Precision is defined as (true-positives)/. Recall rate is defined as (true-positives)/, which intuitively represents the classifier’s ability to identify all positive cases correctly. F1 score is the harmonic mean of precision and recall. Precision and Recall do not have much of a relationship with the formula, but they are mutually restricted in practice. We all hope that the model is accurate, and the recall rate is high, but when the precision rate is high, the recall rate is often low. When β = 1, it becomes the F1-score, in which case both recall, and accuracy are important and have the same weight. In some cases, if we think accuracy is more important, we adjust the β value to be less than 1, and if we think the recall is more important, we adjust the β value to be greater than 1, such as the F2-score.The predicted value is We determined the data list as the first 800 genes from the feature selection list. We used software: Cytoscape and metascape for GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Gene and Genomes) Enrichment Analysis.We used 10-fold cross-validation in the training set to evaluate the performance of the feature selection methods. With leave-one-out cross-validation, the algorithm is repeatedly retrained, which included oversampling, feature selection, and classification model, leaving out one sample in each round and testing each sample on a classifier that was trained without this sample. The framework of the 10-fold CV is shown in R2-score of 10-fold cross-validation of the two feature selection methods is very high. The average R2-score was 96.23 and 96.38%  for the chi-square test as feature selection and random forest as feature selection. Although these two results are very close, the R2-score of Random Forest is slightly higher than the Chi-Square within the same feature number range, and the Rise of R2-score of random forest is more stable, as shown in R2-score, the Random Forest is used for feature selection in the next flow.The results are shown in R2-score as the model evaluation index, 10-fold CV was carried out in the training data, and finally, the parameters, n estimators = 250, max depth = 7, min child weight = 1, in the optimal model of XGBoost were obtained. The results of this model in leaving out one data are shown in We selected 800 genes with Random Forest characteristics, using XGBoost as a classifier. Taking the For each sample, the type of tumor predicted was compared with the type diagnosed. When the predicted tumor type matches the reference diagnosis, it is a true positive. When the predicted tumor type does not match the diagnosis, the sample is considered a false-positive. For each cancer, sensitivity was defined as the ratio of true positive results to the total positive samples analyzed, and specificity was defined as the ratio of  to . To better measure the classification results, we took sensitivity and specificity as the horizontal axis and the vertical axis, respectively, and drew the ROC (Receiver Operating Characteristic) curve to the results as shown in R2-score average of independent testing results is 83.3%, which obtained 42 samples cover five cancers. The trainer had good generalization for COADREAD (Colon Adenocarcinoma and Rectum Adenocarcinoma), LIHC (Liver Hepatocellular Carcinoma), LUAD (Lung Adenocarcinoma), and OV (Ovarian Serous Cystadenocarcinoma), and the R2-score respectively was 1, 1, 0.92 and 0.82, shown in The model was trained according to N estimators = 250, Max depth = 7, and min child weight = 1 in the whole training data for independent testing. The We often use molecular experiments to distinguish the origin of metastatic cancer. Our supporting results combined with the literature review found that the accuracy of cancer classification was low for fixed cancer types, which was similar to other data methods. We selected 16 genes, shown in Genes control protein expression. A gene contains introns and exons, in which the coding region of the protein is encoded. Gene coding of a protein is a DNA-mRNA- protein process. The genes we analyzed are all protein-coding genes.WT1 is a tumor suppressor gene associated with the development of a Wilms’ Tumor, for which it was named. This gene encodes a transcription factor that contains four zinc-finger motifs at the C-terminus and a proline/glutamine-rich DNA-binding domain at the N-terminus. CCL16 is one of several cytokine genes clustered on the q-arm of chromosome 17. Cytokines are a family of secreted proteins involved in immunoregulatory and inflammatory processes. The CC cytokines are proteins characterized by two adjacent cysteines. The cytokine encoded by this gene displays chemotactic activity for lymphocytes and monocytes but not for neutrophils. This cytokine also shows a potent myelosuppressive activity and suppresses the proliferation of myeloid progenitor cells. The expression of this gene is upregulated by IL-10. The CDH17 gene is a member of the cadherin superfamily, genes encoding calcium-dependent, membrane-associated glycoproteins. Diseases associated with CDH17 include Metanephric Adenoma and Cleft Lip/Palate-Ectodermal Dysplasia Syndrome, which is provided by RefSeq et al. Histones are basic nuclear proteins that are responsible for the nucleosome structure of the chromosomal fiber in eukaryotes. Nucleosomes consist of approximately 146 bp of DNA wrapped around a histone octamer composed of pairs of each of the four core histones . Among its related pathways are Transcriptional misregulation in cancer and Activated PKN1, which stimulates transcription of AR (androgen receptor) regulated genes KLK2 and KLK3. HNF1A encodes a transcription factor required for the expression of several liver-specific genes. Diseases associated with HNF1A include Maturity-Onset Diabetes of the Young, Type 3 and Diabetes Mellitus, and Insulin-Dependent 20.To better understand why those genes could tell the origin of the primary lesion, we performed the enrichment analysis using the 800 selected genes. The results of KEGG (Kyoto Encyclopedia of Gene and Genomes)  and GO  are showThe 800 selected genes were significantly enriched in some cancer-related pathways. Cell adhesion molecules (CAM)  played iThe 800 genes were also significantly enriched in some organ-specific pathways. The selected genes were representative in thyroid hormone synthesis, pancreatic secretion, and fat digestion—absorption pathways. Since those pathways were organ-specific, we could show that the random forest algorithm found the differentially expressed genes among different organs.Nowadays, CUP cases are characterized by small primary tumors (difficult to be detected by existing technologies) , primaryR2-score of the classification based on XGBoost can reach 96.38%, while the average accuracy of the support vector machine (SVM) classifier is 82–89%  which is, LUAD (lung adenocarcinoma), OV (ovarian serous cystadenocarcinoma).In this paper, we show that our result is better than in recent studies. Our average s 82–89% . We trais 82–89% , and thiAlthough we have made progress in these studies, there are also limitations. Our test data are collected from 8 series, and there was some detection method between each series. This may be due to the fact that our test results are not as high as the cross-validation results.Further studies could be done in several main aspects. First, the SNP (single nucleotide polymorphism) or methylation data may be combined with expression profiles to further improve the prediction utilities to infer primary lesions for metastatic tumors. Second, the eQTL (expression Quantitative Trait Loci), which supplies us with new insights between expression profile and mutation profile, might also help determine the primary lesions.These findings suggest that by combining multiple tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. At the same time, it can also be used as an orthogonal diagnostic method to utilize the machine learning model processing for auxiliary diagnosis methods.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.KL and ZH designed the study. SC, WZ, JT, and BW collected the data, analyzed the data, interpreted the data. SC wrote the manuscript. JL, XM, and GT reviewed the manuscript. All authors contributed to the article and approved the submitted version.BW, XM, and GT were employed by the company Geneis Beijing Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.