The prognosis of advanced gastric cancer patients remains unfavorable. Molecular heterogeneity has proven to be a major determinant of clinical outcomes. We characterized the transcriptome of the two major subgroups by highlighting the different biological and molecular pathways. We explored their association with clinicopathological features and survival. This comparative study aimed to define a reproducible in silico analysis so that the molecular mechanisms underlying carcinogenesis, disease natural history and the identification of new therapeutic targets can be traced.Diffuse group was characterized by an epithelial-mesenchymal transition (EMT) and inflammatory response, while the Intestinal group was associated with a cell cycle and drug resistance pathways. In particular, the regulons of Diffuse MRs, such as Vgll3 and Ciita, overlapped with the EMT and interferon-gamma response, while the regulons Top2a and Foxm1 were shared with the cell cycle pathways in the Intestinal group. We also found a strict association between MR activity and several clinicopathological features, such as survival. Our approach led to the identification of genes and pathways differentially regulated in the Intestinal and Diffuse GC histotypes, highlighting biologically interesting MRs and subnetworks associated with clinical features and prognosis, suggesting putative actionable candidates. Gastric cancer (GC) molecular heterogeneity represents a major determinant for clinical outcomes, and although new molecular classifications have been introduced, they are not easy to translate from bench to bedside. We explored the data from GC public databases by performing differential gene expression analysis (DEGs) and gene network reconstruction to identify master regulators (MRs), as well as a gene set analysis (GSA) to reveal their biological features. Moreover, we evaluated the association of MRs with clinicopathological parameters. According to the GSA, the  Intestinal, Diffuse and mixed subtypes [Intestinal subtypes from the Lauren classification  and Lauren’s Diffuse types . WHO classification also includes mixed carcinoma and other less frequent gastric tumors. Nonetheless, categorization into a few macro-groups is burdened by excessive internal heterogeneity, resulting in conflicting evidence based on histopathological phenotypes for predicting patient prognosis or response to therapy. Additionally, the GC diversity also hinders the identification of prognostic markers from a molecular point of view . Next-generation sequencing contributes to the establishment of new molecular classifications of gastric cancer, focusing on biological characteristics. The Cancer Genome Atlas (TCGA) identified four genomic gastric cancer subtypes: Epstein-Barr virus-associated (EBV), microsatellite instable (MSI), genomically stable (GS) and chromosomal instability (CIN) groups [Intestinal and Diffuse subtypes and the underlying gene networks responsible for the different biological behaviors. To the best of our knowledge, few studies have focused on the characterization of molecular differences within Lauren’s classification [Intestinal and Diffuse subtypes and explored their associations with clinicopathological features and survival. This comparative study aimed to define a reproducible analysis so that the molecular mechanisms underlying carcinogenesis, disease behavior and the identification of new therapeutic targets can be traced.Gastric cancer (GC) was responsible for 769,000 deaths in 2020, ranked sixth for incidence and third for mortality . Althougsubtypes . Additiosubtypes  has been) groups ,13,14,15) groups ,17. In tfication ,19, and https://www.cancer.gov/tcga (accessed on 15 June 2021)) and the Genotype-Tissue Expression (GTEx) project ) are repositories, collected with high throughput, of clinical data of many tumors and normal tissues from many organs. Although unifying cancer and normal RNA sequencing data from diverse sources represents a bioinformatics challenge, TCGA and GTEx RNA-seq data were successfully merged by Wang et al. enabling cross-study analysis of RNA-sequencing data [https://xenabrowser.net/datapages/ (accessed on 15 June 2021)), we retrieved the dataset (TCGA TARGET GTEx transcript expression by RSEM using UCSC TOIL RNA-seq recompute), clinical data and gene annotations. After crossing the RNA-seq dataset and clinical data, we obtained the data of 380 gastric cancer RNA-seqs, 32 adjacent normal (non-diseased) tissues and 112 normal samples. All the information about TCGA samples’ clinical data, pathology reports and tissues were easily retrievable by the cBioPortal ) website, while the “GTEx Tissue Harvesting Work Instruction” provided all the available information about the GTEx tissues. From the GEO repository [TGCA . Case selection criteria were histologically confirmed adenocarcinoma of the stomach, surgical resectioning of primary GC, age ≥18 years and complete pathological, surgical, treatment and follow-up data. Patients in the GSE15459 dataset from the National Cancer Centre in Singapore had a median follow-up period of 13.47 months, and 91 patients died by the end of the study period. Histopathological data are provided in Ooi CH et al. [This study included GC patients who underwent upfront gastrectomy for a malignant disease nbetweem January 2014 and March 2019 and were collected in the TGCA dataset. The GSE62254 dataset included Signet-Ring Cells (SRC),Diffuse Intestinal mucinous,Intestinal papillary,Intestinal tubular,Intestinal not otherwise specified (iNOS),Stomach not otherwise specified (sNOS).RNA-seq and clinical data of the GTEx and TCGA databases were used to investigate transcriptomic profiles of the GC and normal gastric tissue, as performed by Russi et al. . The preDiffuse” (n = 62). On the other hand, Intestinal samples behave in a heterogeneous way, considering the association with clinical features and the survival results. We filtered out the papillary samples due to their scarcity (n = 7). Moreover, by performing a differential expression analysis , we included, in the “Intestinal” category, the tubular samples, representing most of the Intestinal adenocarcinomas [Intestinal subgroup and further investigation of the possibility of retaining the iNOS. In this way, after differential gene expression analysis  between the Diffuse and the union of the Intestinal (tubular and iNOS) samples, we created a Venn diagram , showed great overlap with the Diffuse vs. tubular (88%) and the Diffuse vs. iNOS genes (80%). From now on, we refer to the tubular and iNOS as “Intestinal” (n = 140). Our final categorization, based on molecularly homogeneous histological types, was:Diffuse ,Intestinal (tubular and iNOS).Initially, the sNOS and SRC samples were filtered out due to the missing histology and unclear different behaviors , respectrcinomas , togetheagram in . The 682The rationale of the study is summarized through a schematic workflow in We evaluated, with chi-square test, the association of the selected clinical variables and the histological type. We depicted the significant association with correlation plots. Moreover, by multivariate Cox proportional hazards analysis, we calculated the overall survival (OS), the disease-specific survival (DSS), the disease-free survival (DFS) and the progression-free interval (PFI) associated with the above clinical features. We applied the Akaike information criterion (AIC) based on a stepwise procedure to obtain the best candidate for the final Cox proportional hazards models, which were depicted as forest plots.edgeR [limma implementation was used for the microarray data. According to the clinical data, we selected 62, 135 and 75 Diffuse samples and 140, 146 and 99 Intestinal samples from the TCGA, ACRG and GSE15459 datasets to perform a differential expression analysis. According to the explorative analysis, the pathologic stage and N feature of the TNM staging system were used as blocking factors in the TCGA data generalized linear modeling. A gene was considered as differentially expressed (DEG) if (1) corrected (FDR) p-value < 0.05 and (2) expression change > |1.5|-fold (log2FC > |0.58|).The regression-like model implemented by the edgeR  was usedClusterProfiler [p-value < 0.05. We retrieved, from the aracne.networks [mra function implemented in the corto [logcpm (logarithmic counts per million) of the genes in the dataset. The enrichment score (ES) reflected the degree to which a regulon was overrepresented at the top or bottom of a ranked list of genes. The higher the ES, the more active the subnetwork associated with the candidate MR. Moreover, to highlight the biological function of the MR’s regulon, we evaluated its overlap with the enriched gene sets. A hypergeometric test was performed to identify the significant overlaps. Statistical analysis was performed using the computing environment R  [The overrepresentation of the Molecular Signatures Database’s (MSigDB)  hallmarkProfiler . We consnetworks , the AP-networks  inferredhe corto , we scorAustria) .To better describe the heterogeneity of the two groups, we performed both ssGSEA and ssMRA to obtain the activation or deactivation normalized enriched score (NES) for each gene set or each MR’s inferred small subnetwork (regulon) in each sample.ggplot2 [We evaluated, by linear modeling, the association of the selected clinical variables and each MR’s profile obtained by ssMRA. We depicted selected interesting associations with a ggplot2 . MoreoveDiffuse, 12 as signet, 139 as stomach NOS, 17 as Intestinal mucinous, 7 as Intestinal papillary, 71 as Intestinal tubular, 69 as Intestinal NOS and 3 with no clinicopathological annotation. The patient characteristics and clinicopathological data are in Intestinal type, and >50% were pathological stage ≥III. An association of the histological groups described above resulted in the pathologic stage, lymph nodes ratio . Via multivariate Cox proportional hazards analysis, we investigated the association of clinical features with the overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS) and progression-free interval (PFI). The best-fit model according to Akaike Information Criterion (AIC) for the overall survival comprises the histology and pathologic stage , while histology, pathologic stage and microsatellite status best model the DSS . Forest plots of the stepwise selected models are depicted in Diffuse and 140 as Intestinal (including 71 tubular histotypes). The association profiles of the new Intestinal  types and clinical data according to the χ2 test as well as with survival. Therefore, no other variables influenced the downstream analyses which focused on the molecular and functional characterization of GC subtypes.Overall, there was no significant association between the histological  and the normal mucosa samples  by obtaining 12,106 upregulated and 5562 downregulated genes. We subsequently defined 4076 upregulated genes in the Diffuse vs. Intestinal samples and 2751 upregulated genes in the Intestinal vs. Diffuse samples. Finally, after the intersection between the GC upregulated and Diffuse or Intestinal upregulated genes, according to the Venn diagram in Diffuse vs. Intestinal samples as a Diffuse signature. We performed several hierarchical steps of differential gene expression and functional enrichment analyses to evaluate the biological differences between the two GC histological subgroups and pinpoint respective candidate biomarkers. We first defined the differentially expressed genes between the GC  using the mSigDB gene sets comprising, among others, hallmark and GO gene sets. The GSA results  are reported in Similarly, we defined as an  samples . Among tDiffuse tumors appeared to enrich a higher number of gene sets for all categories except for the positional one. Hallmark categories related to the epithelia-mesenchymal transition (EMT) and cellular cycle defined a clear biological difference between the two GC subtypes . Indeed, the high endogenous estrogen exposure in women was indicated to be a protective factor, which explains the higher incidence of Intestinal GC in men [Intestinal type of GC were those involved in cell division, chromosome segregation and embryo development, delineating a different biology for this GC histological type , 2058 were active in Diffuse GC while 1550 were active in Intestinal GC. Beyond the statistical significance of MRs, we focused our study on those that were likely relevant to cancer biology features by overlapping each MR’s regulon with significantly enriched gene sets from the GSA analysis. In this way, we aimed to give a biological characterization of the data-driven regulons which were significantly overrepresented by MR analysis. Hallmark gene sets, more than the others, immediately summarized and suggested specific well-defined biological states; thus, we focused on MRs overlapping with them  are reported in Diffuse and Intestinal GC samples as depicted in Although the two histological subgroups investigated here are widely recognized in clinical practice, their consistency is weakened by molecular and clinical heterogeneity. We aimed to highlight the more homogeneous subnetworks for each subtype and pinpoint subnetworks related to patient outcomes and tumor features, even if not related to the histological division. Accordingly, we highlighted some interesting MR examples. We performed an ssGSEA and obtained the grade of activation or deactivation (NES) for each gene set in each sample by identifying those MRs capable of influencing patient outcomes and GC natural history. The heatmap in Diffuse but not Intestinal samples. Moreover, in Intestinal vs. Diffuse GC only in the MSS samples. These examples showed the possibility of exploring the association of an MR with any clinical feature when its behavior is homogeneous or different between subgroups or is different among feature levels. Lastly, in Diffuse but not Intestinal samples, suggesting a putative gender- and histology-driven rethink of treatment strategies. Using the Cox proportional-hazards model, we described the change in OS, DSS, DFS or PFI if the NES of the MRs rose by one unit. In p-value < 0.05). Interestingly, the MR CGB5, having the higher hazard ratio (HR) considering OS, DSS and PFI results, was associated with a worse prognosis while the MR ARHGAP33 results, having the lower HR considering DSS and PFI and the second lowest by OS, were associated with a better prognosis. For each patient and for every additional NES unit of the MR CGB5, the risk of death, disease-specific death and disease progression increased by 79% (HR 1.79), 152% (HR 2.52) and 112% (HR 2.12), respectively. On the contrary, for each patient and every additional NES unit of the MR ARHGAP33, the risk of death, death by disease and disease progression fell by 30% (HR 0.70), 44% (HR 0.56) and 48% (HR 0.52), respectively. When we looked at the Cox proportional-hazards model results, focusing on the MRs previously highlighted, it was very easy to pinpoint the HR of the VGLL3, INHBA and PRRX1 regulons, 27% (HR 1.27), 44% (HR 1.44) and 46% (HR 1.46). The worst prognosis, related to a higher activity of regulons, was associated  with Diffuse tumors and a greater tumor mass (TNM:T). In addition, among the worst prognosis of MRs associated with EMT, there was SNAI2 (OS HR: 1.51 and DSS HR: 1.52). ILF3 regulon was associated with a better prognosis (DFI HR 0.53 and PFI HR 0.75) and with high activity in Intestinal tumors . Moreover, the ACIN1 (an apoptosis-related gene) regulon . According to Townson SM et al. [Intestinal tumors was strongly associated  with a better prognosis . Finally, our results were validated in the ACRG and GSE15459 cohort, by performing differential gene expression analyses on GSA and MRA in Diffuse and Intestinal GCs. We reported, as Venn diagrams, the overlaps among the DEGs  and Intestinal (tubular and iNOS). We highlighted that there was no significant association between these groups and clinicopathological data as well as survival. According to this, the clinical and survival differences reported above could be imputed to the SRC and non-tubular Intestinal groups. Subsequently, we deployed a computational pipeline aimed to highlight the key genes, networks and pathways characterizing the two groups. Differential expression analysis produced two refined lists of GC histology-related candidate biomarkers. Enrichment analyses, as well as master regulator analyses, highlighted a greater number of activated gene sets and regulons in Diffuse than in Intestinal GC. The MRs, by their nature, represented a robust way to describe key genes and network features of a characterized group. The regulons, being data-inferred subnetworks, might have a cryptical biological meaning so the overlap with significantly enriched gene sets strongly hints about their function as a biological labeler. This step allowed us to select activated subnetworks showing literature-derived, biologically interesting features but retained possibly related non-literature-derived gene connections. The ssMRA allowed us to investigate the homogeneity of the subgroups’ MRs, pinpointing which MR might be a better candidate for this classification and which one could lead to a different classification or treatment strategy. As matter of fact, the activity of many MRs showed an association with survival while there was no difference between the Diffuse and Intestinal subgroups, hinting at a possible different classification. Interestingly, two independent datasets validated many of the results confirming the goodness of the computational pipeline and showing the robustness of the MR analysis across different datasets. It is important to remember that the MR represents an entire network (the regulon), suggesting the underlying molecular explanation of the observed biological behavior. For example, the Cox proportional-hazards model associates the MR VGLL3 with a worse prognosis and the Fisher test associates VGLL3 with Diffuse tumors and a greater tumor mass (TNM:T), according to a previous report [Despite its decreasing incidence, GC remains one of the most common causes of death for neoplasm worldwide. Recent epidemiological trends indicate a relative increase in the rate of the ountries ,43,44,45s report . In thiss report . SNAI2 is report . Diffuse type of GC was also characterized by the dysregulation of immune signaling that, according to Hill et al., created a favorable microenvironment promoting tumor progression, invasiveness, angiogenesis and metastasis [Diffuse type of GC. Interestingly, IFN-γ seemed to also be a driver of disease progression during chronic gastritis to metaplasia by direct killing gastric parietal cells [Diffuse or Intestinal GC subtypes remark the biological features outlined by hallmarks. The overlap between the three MRs highly enriched in Diffuse GC and positional gene sets seemed to define an activation of the T cell-mediated response. Indeed, in the chr1q23 cytogenetic band localized genes were involved in lymphocyte activation and antigen presentation to T cells . On the other hand, Intestinal GC was characterized by MRs  involved in mitotic spindle assembly, G2/M checkpoint regulation and expression of E2F target genes, suggesting a proliferative phenotype. The association between a proliferative GC subtype and Intestinal histology was previously reported to be characterized by the upregulation of the centromeric family of proteins (CENPs), among them CENPF [Intestinal GC, the chrXq28 band resulted in significantly associated activity between SSX1, SSX4 and SOLHLH1 MRs, which are involved in stem cell maintenance and are indicated as cancer and testis antigens [Intestinal GC enrichment of HNF1 transcription factor target motifs suggested a marked transcriptional activity and modulation of cholesterol and sterol homeostasis, essential for cell division [Our study showed that the tastasis . The roltastasis . CIITA atastasis ,50,51, aal cells . Similaral cells . A pro-iem CENPF . Authorsem CENPF , highligem CENPF . Interesem CENPF . Accordiantigens . The enrantigens , GC tranantigens  and teloantigens . Interesantigens ,62. In adivision . HNF1 prdivision .Diffuse and Intestinal gastric cancers, suggesting molecular bases for clinicopathological differences between the two histotypes. In particular, Diffuse GC is characterized by the alteration of pathways involved in immune cell regulation and extracellular matrix organization, while the Intestinal type is associated with impairments to the cell cycle regulation pathways and alteration of response to growth factors and hormones. Overall, the histological differences between Diffuse and Intestinal GC are based on genetic and epigenetic factors and neither of these two ways can be ignored. Many studies have shown that unveiling the molecular complexity of cancer can help predict specific cancer biomarkers  and design biological network-based anti-cancer therapies [Our results showed that distinct biological features characterize herapies ,22,23 by


The human genome is marked by several singular and combinatorial histone modifications that shape the different states of chromatin and its three-dimensional organization. Genome-wide mapping of these marks as well as histone variants and open chromatin regions is commonly carried out via profiling DNA–protein binding or via chromatin accessibility methods. After the generation of epigenomic datasets in a cell type, statistical models can be used to annotate the noncoding regions of DNA and infer the combinatorial histone marks or chromatin states (CS). These methods involve partitioning the genome and labeling individual segments based on their CS patterns. Chromatin labels enable the systematic discovery of genomic function and activity and can label the gene body, promoters or enhancers without using other genomic maps. CSs are dynamic and change under different cell conditions, such as in normal, preneoplastic or tumor cells. This review aims to explore the available computational tools that have been developed to capture CS alterations under two or more cellular conditions.  Chromatin is composed of DNA that is wrapped around proteins and condensed to fit into the cell nucleus. The fundamental unit of chromatin is the nucleosome, which is made up of eight histone proteins around which 147 bp of DNA is coiled. Histone tails can be altered with several different modifications, such as methylation and acetylation . Some hiThe application of statistical models to epigenomic datasets consisting of histone modifications, histone variants or chromatin accessibility data has led to the identification of combinatorial histone marks known as chromatin states (CS) , 7. SincThese CSs help functionally annotate the noncoding genome with higher precision without prior knowledge of the genomic elements present. Annotation of the whole genome including noncoding regions using functional biological and genomic labels is an essential step in identifying the regulatory elements of the genome. It allows us to subsequently explore changes in their function under varied cellular conditions such as dysplasia or neoplasia. To this end, several methods have been developed to date that perform CS identification and compare these chromatin labels across the genome between multiple samples or conditions.A commonly used assay to study chromatin is based on the immunoprecipitation of chromatin using antibodies against proteins bound to it at regions such as histone marks, followed by sequencing (ChIP-seq). More recent methods with fewer limitations that aim to interrogate chromatin are cleavage under targets and release using nuclease (CUT&RUN) and cleavage under targets and tagmentation (CUT&Tag). These methods are considered alternatives to the traditional ChIP-seq methodology . To geneOnce genome-wide profiles of essential histone proteins are obtained, chromatin marks can be investigated individually, or, using various computational methods, combinatorial histone marks or CSs can be identified. A widely known tool for annotating the noncoding genome is ChromHMM, which identifies CSs throughout the genome . ChromHMAnnotation of the genome and mapping of CSs is a significant step toward a better understanding of the functions of genomic segments . The nexTo date, several tools have been developed to identify differential CSs under different cellular conditions. These tools include but are not limited to ChromstaR , 29, ChrCurrent methods could be divided in two main groups, (a) methods that can compare CSs maps at the genome-wide level, and (b) methods that can be used for the comparison of CS at a specific genomic region. This classification can define the scope of applicability of each method. In addition, with a few exceptions, almost all methods assume CSs as binary entities as opposed to quantitative variables. Another aspect that determines application of each method is their input formats. CS maps are the usual input of these methods; however, some methods can take singular histone mark as input.Hereinbelow, we provide a brief, simplified and step-by-step overview of the methodology of each tool along with a general comparison to help readers choose the best and most efficient tool for application to their epigenomic datasets.ChromstaR is an algorithm that computes combinatorial CS changes across several conditions. It uses a multivariate HMM to determine distinct combinatorial histone marks from ChIP-seq experiments. Based on the presence or absence of modifications in each condition, CSs are assigned to all regions across the genome. Aligned reads from each sample are used as input for ChromstaR to carry out the following steps. The algorithm partitions the genome into nonoverlapping bins and calculates read counts that map into each bin. The read count distribution is considered a two-component mixture of zero-inflated negative binomials. One component is the low read count bins, which account for the background noise, and the high read count bins, which represent the signal. A univariate HMM with two hidden states is applied to annotate each bin for the presence or absence of histone marks. To perform joint analysis on all samples simultaneously, a multivariate HMM is used to assign each bin to one of the multivariate components.ChromstaR has four different modes. (1) The full mode: recommended when the number of marks multiplied by the number of conditions is less than or equal to eight, resulting in a maximum of 256 combinatorial states. (2) The differential mode: outputs significant differences between conditions. The accuracy of the states is lower than that of the full or combinatorial modes. (3) The combinatorial mode: analyzes all measured histone modifications at once. This yields CSs with higher accuracy, and it can be applied for any number of marks and conditions. This mode has a higher false positive rate for differences between conditions compared with the differential or full modes. (4) The separate mode: wherein replicates are processed in a multivariate manner.In ChromstaR, several theoretically possible combinatorial histone modifications are nearly absent at the genome-wide level. This could be a result of biochemical barriers to the coexistence of two or more histone modifications on the same or adjacent amino acids. Furthermore, some genomic regions are devoid of any measured histone marks, and these are called ‘empty states.’ These regions are subject to change based on the number of histone marks and cell type analyzed.Some of the advantages of ChromstaR over other approaches are as follows: (1) it is independent of chromatin segmentation and annotation tools in identifying CSs; (2) the number of CSs does not need to be defined; (3) unlike probabilistic models, each state cannot include multiple and overlapping combinatorial states but is based on patterns of histone modifications; (4) narrow and broad peaks can be analyzed in a combined manner; and (5) replicates can be included as separate experiments with no prior merging. The workflow of ChromstaR is briefly illustrated in P values as input. It also provides an option to filter features based on defined thresholds of these values. To adjust for the technical variation between samples, which is common among DNA–protein profiling techniques , the algorithm normalizes these values per sample at the genome-wide level. Chromswitch then creates a sample-by-feature matrix and performs hierarchical clustering using two different strategies, as shown in Chromswitch detects spatial, temporal or tissue-specific CS changes in a defined genomic region using peaks or CS calls derived from a DNA–protein or chromatin accessibility profiling methods such as ChIP-seq or DNase-seq. This method takes genomic coordinates along with their corresponding fold changes and An advantage of Chromswitch is its applicability to small sample sizes and high class imbalance. The algorithm also performs well for histone marks with sparse signals and broad domains.k-means clustering is then performed on the chromatin sample space for a range of groups (n = 2–50), and the optimal number of clusters is determined using the Calinski–Harabasz Index (CHI). A second space, the chromatin region space, is also generated using MCA analysis and is a projection of the vectors reflecting every CS combination into the MCA space. Chromatin determinant regions (CDRs) are obtained by choosing those genomic regions that overlap with cluster sample fingerprints. The cluster sample fingerprints are vectors representing chromatin patterns that are unique to each group of samples identified by chromatin sample space. Each of the epigenomic regions is then associated with its nearby fingerprint in the chromatin region space. Lastly, CDRs are defined as genomic positions for which all CSs are among the top 10 ranked shortest distances to their fingerprints in the chromatin region space. This combination of fingerprints partitions the sample clusters.ChromDet is a method to classify samples from multiple cell types by leveraging unique epigenomic features that are common across subsets of samples. Given the hypothesis that cellular phenotypes are directly related to their epigenetic composition and the contribution of chromatin changes during differentiation to cell fate, ChromDet aims to identify chromatin changes across the genome among various cell types. The algorithm can identify chromatin drivers corresponding to a set of regions that are altered during phenotypic transitions. ChromDet is a modified version of an earlier tool, S3det . It is bChromDet can be utilized to detect CS changes for DNA–protein profiling methods such as ChIP-seq, CUT&RUN and CUT&Tag. Similar to other approaches, for these datasets, the raw data should be processed using a common pipeline (i.e. the ENCODE ChIP-seq pipeline or CUT&RUNTools), and peaks and other output files are generated . Next, tOne of the additional features of ChromDet is defining an epigenomic divergence index by calculating the ratio of CDRs with a CS different from the healthy cell condition. This feature represents the level of divergence of a sample from the healthy sample space, which is based on normal samples. The ChromDet workflow is illustrated in ChromDiff is a statistical pipeline for groupwise CS comparisons. It is broadly applicable for epigenomic comparisons and for studying CS differences at the genome-wide level. ChromDiff addresses the question of how CS segmentation differs under various conditions or tissue types. It captures discriminating epigenomic features between two groups by comparing their CS patterns. The algorithm generates an information-theoretic representation of the epigenomic data and applies a covariate correction for large-scale sample analysis. ChromDiff calculates statistics of recurrent CS changes and associates the identified regions with their corresponding protein-coding genes. The resulting gene sets can be further utilized to explore dysregulation in biological pathways between the two groups. Because ChromDiff focuses on gene body regions, downstream analyses can be performed on the resulting gene sets, such as gene set enrichment analysis to investigate biological processes associated with epigenomic differences. The expression level of differential genes can also be compared with the epigenomic features. Moreover, hierarchical clustering can be applied to identify cluster-specific gene sets.t test or F test with multiple hypothesis correction. Finally, the pipeline outputs significantly different CSs and gene combinations between the groups. ChromDiff can also be used to cluster differential regions with similar CS signatures into distinct subgroups, thereby highlighting subgroup-specific gene set enrichment and expression patterns.This method takes a gene-centric approach and defines epigenomic features by calculating the probability of CS labels for each gene in each sample. This representation significantly reduces the CS data and at the same time preserves the information necessary to calculate the information theory metrics. Next, the algorithm generates a feature × sample matrix and applies a logistic regression model to correct for feature covariates. ChromDiff then uses the corrected feature values to detect differences between the two groups using a nonparametric Mann–Whitney–Wilcoxon test, Student’s An advantage of ChromDiff is its ability to correct for external covariate factors, which is an essential step in differential analysis when using datasets with experimental and sample differences as well as variation due to batch effects. This feature was not part of prior algorithms such as dPCA. The ChromDiff workflow is briefly illustrated in x is in state i and a replicate from group y is in state j, that genomic position is assigned to a precalculated score ijs that measures the dissimilarity between states i and j. Positive scores indicate dissimilarity and negative scores indicate similarity between states. In this way, chromosomal segments with high cumulative scores are identified. The cumulative score is referred to as the differential chromatin score (DCS), which is an indicator of the dissimilarity between CSs. Overlapping identified regions are summarized across all samples by the average of their DCS and by applying the union of their coverage. DCSs for each segment are converted into expected values (E) to allow the ranking of candidate regions based on their statistical significance and additional analysis. Furthermore, the identified DCDs correlate with differentially expressed genes between groups. One advantage of SCIDDO over similar methods is its applicability on a genome-wide scale and its high performance in a limited number of samples. The workflow of SCIDDO is illustrated in SCIDDO is a score-based method that identifies differential chromatin domains (DCD). It uses CS segmentation maps to compute CS dissimilarities in multiple groups or across various cell types. SCIDDO essentially has two main steps. In the first step, CS emission probabilities are used to calculate pairwise CS dissimilarities. The second step is a differential analysis by comparing individual replicates from one group with the replicates from a second group or biological condition. Next, at each position of the genome, if a replicate from group EpiAlign is a pairwise alignment-based tool that compares CS sequences learned from several datasets and identifies locally aligned chromatin regions. The algorithm compares pairs of CS sequences by computing a local alignment score. The program detects the alignment between CS sequences using both mismatch and deletion scores. These scores are based on the weight of each CS in the sequence. A CS segmentation method is first applied to the datasets to obtain a single-track CS sequence. Consecutive occurrences of a similar CS on each epigenome are compressed into one individual state label, and then EpiAlign performs a local alignment-based on the compressed state labels of the two genomic regions. Compressed state sequences are generated since most uncompressed sequences contain long repetitive CSs that are mostly quiescent or low. Therefore, this method allows one to focus on the CS pattern rather than a single CS that spans a long genomic region. To perform CS sequence alignment, EpiAlign uses a modified Smith–Waterman algorithm, which is based on triplets of matches, mismatches, and gaps. EpiAlign can extract similar CS pattern from one epigenome or across multiple epigenomes. EpiAlign uses a vertical alignment strategy to compare CS sequences of coding genes across multiple epigenomes. The vertical alignment strategy is applied to the compressed CS labels assigned to the genomic region of interest. This approach compares alignment scores across multiple epigenomes.A notable feature of EpiAlign compared with that of other similar methods is its effective use of sequential information of CSs by generating compressed CS labels. Sequential CSs harbor essential information on the mechanisms of gene regulation . EpiAligdPCA is an algorithm that uses a small number of principal components to infer differential protein–DNA interactions from multiple ChIP-seq datasets from two biological conditions. dPCA uses unsupervised pattern discovery to identify the main patterns of differences between the conditions using differential principal components (dPCs) that represent the covariation pattern of quantitative signals among several proteins or histone marks. The algorithm then identifies differential genomic loci for each of the main dPCs and ranks them based on the magnitude of differences. Statistical significance for each locus is determined by the comparison of differences between conditions with the background variation among replicates.k-means clustering use. This characteristic makes the patterns discovered by dPCA more reproducible. The workflow of dPCA is briefly illustrated in dPCA is a deterministic algorithm that does not require random initialization or the ad hoc choice of parameters, which some other methods such as EpiCompare is composed of three algorithms that identify genome segments with unique epigenomic features across different cell types. For these algorithms, users must define foreground samples, or the group for which unique epigenomic features are being sought, and background samples, or the group of samples against which the foreground samples are being compared. EpiCompare identifies chromatin regions enriched in the foreground group compared with the background samples.q values. The third algorithm performs k-means clustering based on the Jaccard index distance on regions in the binary data table for each feature. The optimal number of clusters is determined by the elbow and Silhouette methods. Feature density is calculated for each cluster, and it is defined as the percentage of regions with the feature in each cluster. The clusters unique to each cell type are defined as those with a higher feature density compared with that of the background set.The first algorithm sets a frequency cutoff for each feature, and samples having the feature in either the foreground or background set are calculated (the default cutoff is 80% in the foreground samples and 20% in the background samples). Features that fulfill these criteria are considered positive regions and are ranked based on the percentage difference. The second algorithm applies Fisher’s exact test between the number of samples with the specific feature in the foreground and the background samples. All identified features are ranked based on the calculated Benjamini–Hochberg EpiCompare allows the identification of group-specific epigenomic features such as different types of tissue or lineage-specific regulatory elements. The algorithm allows users to compare different combinations of epigenomic datasets either in their data or in Roadmap Epigenomics. Furthermore, EpiCompare output can be readily visualized in the WashU Epigenome Browser. The EpiCompare workflow is briefly illustrated in In this review, we discussed various methods that identify the epigenomic features that are unique to a group of samples. Despite differences in methodologies, these algorithms detect differential chromatin patterns in multiple groups of samples. Finding such patterns in biological or pathological cell state transitions can aid the understanding of the process of cell transformation. For instance, such analysis could be informative about the processes of cellular differentiation, the impact of environmental stimuli, or even tumorigenesis. The transition of normal cells to cancer cells is accompanied by crucial changes in CSs in which various transcriptionally active regions are suppressed or vice versa . TumorigAlthough most of the described methods use epigenomic features that have previously been inferred from each sample as input; however, ChromstaR and dPCA can take aligned reads and process them further to perform differential analysis . The inpSome of these methods only perform region-based analysis. Chromswitch, EpiAlign, ChromDiff and dPCA can be applied to a user-specified genomic region, whereas other methods apply a genome-wide approach. Although ChromDiff can be applied to any region, the focus of the algorithm is on gene bodies to improve the interpretability of the results. Furthermore, using standard statistical tests, such as the nonparametric Mann–Whitney–Wilcoxon test as in ChromDiff for the identification of differential CSs, restrains the algorithm to reach a sufficient statistical power only by including a large number of replicates in each group. This also applies to algorithms used in ChromDet and dPCA, making these methods more suitable for larger numbers of samples, while it can also be a limitation of these methods to be used for epigenomic datasets with fewer number of samples.None of the tools discussed in this review have been benchmarked for application in single-cell epigenomic datasets. The sparse nature of single-cell epigenomic datasets requires the development of new models and algorithms tuned for such datasets. The ChromVAR and PRISM algorithms are examples of methods specifically developed for single-cell assay for transposase-accessible chromatin (scATAC-seq) datasets, which could be adapted for CS comparisons at the single-cell level .Combinatorial histone marks on the noncoding genome can identify chromatin states (CS).Identification of CSs across the genome enables functional annotation of the epigenome.Various computational methods can be used to interrogate CS changes among different cellular conditions.Stepwise algorithms and distinct features of each method can lead to a better understanding and applicability of these tools for various epigenomic datasets.Summary_Box_R3_bbac439Click here for additional data file.


Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length − 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (4) analyzing differential expression with DESeq2 with adjusted  Small non-coding RNA (sRNA) of less than 200 nucleotides in length are important regulatory molecules in the control of gene expression at both the transcriptional and the post-transcriptional level ,2,3. ResAll major pipelines for sRNA expression analysis may be classified into three groups. First, tools that only allow for a particular stage of expression analysis . Second, the ones that allow for the more expanded analysis of a certain type of sRNA . The tools of the third group provide a means for the expanded analysis of two or more types of sRNA. The characteristics of some existing sRNA pipelines are presented in The two tools that are specialized in the mapping of small RNAs only are similarly named: srnaMapper  and sRNASeveral tools in the second group were designed specifically for piRNA annotation of NGS-samples, namely, piPipes , PILFER A number of tools are available for the analysis of several different types of sRNAs. These are sRNAtoolbox , sRNAnalA comparison of the most popular pipelines is presented in The sRNAnalyzer  is a comSPORTS1.0  is a comiSRAP  is anothOne of the most popular instruments for small RNA annotation is a web-interface-based sRNAtoolbox . This inThe above-mentioned pipelines and others were recently reviewed in detail elsewhere .Several pipelines have been developed for RNA-seq data analysis ,34,35. TThe standard workflow for RNA-seq analysis includes several steps. First, preprocessing involves reads trimming to remove the adaptors or low-quality bases from reads ends, and this may be carried out using specific tools, such as Trimmomatic  or cutadAt the next step, the mapped reads are summarized for a particular transcript annotation. Frequently used approaches for this procedure include featureCounts  from RsuFinally, the obtained counts are normalized and, after filtering, inferred with the statistical model for DE. Commonly used approaches for this step are DESeq2 , edgeR  and limmHowever, adapting the described genome-alignment-based expression analysis workflow poses several challenges. First, due to the very short length of sRNA transcripts, the influence that the samples and library preparation-related parameters (sequencing kit or adapters choice) have on the result of preprocessing and trimming procedures is higher compared to that of mRNA reads preprocessing. Second, the small length of reads makes the alignment procedure more challenging . TherefoThus, the objectives of the current study are as follows. First, to explore the optimal parameters for sRNA-data-specific preprocessing steps (such as the choice of sequencing kit or adapters and trimming threshold). Second, to assess the alingment performance and summarize the sRNA data procedures. Third, to evaluate filtering procedures for lowly expressed transcripts. Finally, to estimate the resulting expression’s signature quality after DE inference.We used small RNA sequencing data (SRA archives) from 7 published human studies: dataset from Wong et al. article   . HoweverThe results for various trimming strategies are presented in The strategy of removing a fixed number of bases was inconsistent and not conserved across datasets and kit types; thus, it was not feasible to observe an optimal trimming length value while retaining a sufficient number of reads for all observed data . The strThe strategy of removing a given fraction of the adapter appeared to be the most conservative across all datasets and kits A. With trocedure B and, foReads were mapped with genome-alignment-based methods  and the prebuilt transcriptome alignment-based method (RSEM) demonstrated sufficient alignment rates . A compaThe highest alignment rate values were observed for bowtie, with one mismatch allowed (bowtie -v 1) , with no mismatches allowed (bowtie -v 0) as the second highest fraction of aligned reads . RNA-seq specific aligners, STAR and hisat2, and RSEM demonstrated lower mean values for alignment ratios . However, for some datasets, hisat2 and STAR had higher alignment rates (74% for “Hua” dataset for hisat2 and 85% for “Huang” dataset for STAR). No alignment rates for RSEM exceeded 70%.Assignment rates and input reads ratios are shown in We observed large variations in assignment rates across datasets and across samples of several datasets, specifically for “Delker” and “Wong” data, that may be explained by the different kits used for sample preparation within one dataset B–D. The RSEM showed low assignment rate values for all datasets. The highest mean assignment rate (assigned/input) was 0.26 for “Morgan“ data. This may be explained by the transcriptome assembly process , which was likely not optimized for short sRNA transcripts B.FigurePseudoaligners kallisto and salmon do not have a specific assignment rate, since reads are probabilistically aligned to transcripts; therefore, the procedure combines the alignment and assignment of reads. As expected, we observed large variations between datasets . The kalTranscript numbers resulting from different filtering strategies (thresholds) are presented in the The mean (counts) > 5 appears to be the optimal filtering approach for expression processing based on the results of transcript numbers and distribution of sRNA biotypes. Using the mean (counts) > 10 and the median (counts) > 5 may be a preferred choice when high numbers of false-positives are found with mean filtering, with the threshold = 5.p-value < 0.05 and q-value  < 0.05. The numbers of DE transcripts with q-value < 0.05 per dataset and pipeline are shown in The results of the DE analysis were obtained using in-built package models DESeq2, edgeR and limma, two filtering options (mean (counts) > 5 and median (counts) > 5) and two thresholds of significance, DESeq2 delivered the highest number of significant DE transcripts with the application of multiple testing corrections filtering; the mean of all pipelines was 20.7 and 12.8, for the mean (counts) > 5 and the median (counts) > 5, respectively. EdgeR returned fewer significant transcripts; the mean for all pipelines was 10.0, which was the same for both thresholds. The lowest number of significant transcripts was produced by limma; the mean for all pipelines was 6.4, which was the same for both thresholds .The DE analysis of the two groups of contrast samples revealed two types of dataset. “Delker”, “Huang”, “Hua” had a fairly large number of significantly DE transcripts, with the mean number for all methods obtained by DESeq2 = 26.8, 44.5 and 68.5, respectively. The other group included “Donkin” and “Ingerslev” datasets, which had a very low number of significant transcripts; the means for all methods were 0.5 and 0.6, respectively. The means obtained by sRNAnalyzer with two allowed mismatches were 5 and 8, respectively.We suggest that this grouping of datasets is predictable, based on the biological differences between the analyzed samples. We do not anticipate big difference between groups in both the “Donkin” and “Ingerslev” datasets, because both obesity (“Donkin”) and exercises (“Ingerslev”) were not associated with sperm RNA expression by any known strong causative link. All other datasets were related to a distinct tissue type and disease state: “Delker”—different tissues and neoplasm vs. no neoplasm, “Huang”—health and disease, “Hua”—embryo quality that may be directly related to sperm program.p-value adjustment , ranging from 0 (the worst) to 1 (the best). Filtering and DE analyses are presented for all datasets in  methods . Limma djustment .For tsRNA expression, read alignment and quantification patterns were similar to those of other sRNA types. Reads assigned to mature tRNA by Rsubread were pseudoaligned by Kallisto to tsRNA sequences. High assignment rates are presented in p-value < 0.05) and across all pipelines   . The nump-value < 0.05  > 5, DESeq2). H-scores higher than 0.6 were only obtained by DESeq2. These values were lower than those observed for sRNA transcripts’ expression analysis, which may be explained by the lower tsRNA signal compared to sRNA.An H-score of higher than e < 0.05 . The higSPORTS provided higher assignment rates than other pipelines for almost all datasets . For ”DeEach stage of sRNA analysis, as indicated earlier, can be conducted by employing various methods and different parameter settings for each method. Nevertheless, some parameters sets or tools may produce better results than others when using metrics for each analytical stage of benchmarking. Thus, we suggest the following pipeline, as described in Using main metrics/benchmarks, we presented the results of the application of this pipeline for all datasets in We used seven publicly available human datasets and metrics/benchmarks for each step of sRNA-seq data analysis, from biosampling and library preparation to DE analysis in an effort to produce an optimized sRNA-seq pipeline. Based on our analysis, we suggest a pipeline that produces robust DE analysis results for sRNA transcripts, at least for categorical factors and two-group comparisons of biosamples.Since each dataset was generated with different tissues, library preparation kits, and sequencing approaches (sequence machine and read length), large variations in data were observed, as expected. We aimed to use existing tools to construct an optimal pipeline for quality sequencing data analysis, despite the differences in input data.To account for data variation in the original datasets, flexible trimming thresholds were applied. The input reads lengths were between 42 nt (“Donkin”) and 150 nt (“Hua”) across datasets. We suggest using 15 nt as a lower bound and Read length − 40% of adapter length as an upper bound of read length for trimming. This approach afforded good adapter removal and avoided significant loss of reads for a range of datasets.There are many tools for the assignment of trimmed reads to obtain usable expression data. Assigning processes can be conducted in different ways, and each has its strength and limitations. Alignment-based methods seem to be less specialized, and we suggest using a bowtie aligner, which provides a high assignment rate and H-score for sRNAs across datasets.We tested six thresholds for data filtering. As expected, thresholds based on the minimum counts of transcripts (5 and 10) resulted in low transcript numbers and may result in the loss of important data . Filterip-values adjustment for weaker-signal data.Three well-established packages based on regression analysis  and two groups of contrast  factors were used for DE analysis. To assess the quality of delivered expression signatures, the H-score metric of the Hobotnica package was employed. We observed a similar medium quality for the data separation test using three packages across datasets. We suggest using DESeq2 with multiple test corrections for data with strong and well-detected signals, and limma with no Tools designed specifically for small RNA analysis  may seem to be more suitable for sRNA seq data analysis. A disadvantage of the sRNA-specific tools is the ’map and remove’ approach, where the order of databases used to sequentially align reads can affect the analysis outcome and different sRNA biotypes are not treated independently. The sRNAnalyzer cannot analyze tRNA or rRNA, and SPORTS cannot analyze piRNA, as depicted in the Trimming with the lower length bound = 15 and the upper-length bound = Read length − 40% of Adapter length;Mapping on a reference genome with bowtie aligner, with one mismatch allowed (-v 1 parameter);Filtering by mean threshold > 5;p-value < 0.05.DESeq2 for DE analysis with adjusted It should be noted that analyzed datasets demonstrated heterogeneity according to the processing steps. We suggest that the best pipeline depends on the input data , hypothesis of the study, and sRNA-seq data . In this study, continuous factors and complex models with covariates were not analyzed. Nonetheless, for categorical factors and two groups of biosamples, our optimized pipeline for sRNA analysis recommends the following steps:Although we investigated human miRNA, piRNA, tsRNA data, we anticipate that our optimized pipeline may be employed using similar sRNA data from other organisms.Small RNA sequencing data (SRA archives) from 7 published human studies were retrieved from GEO database and extracted to FASTQ reads using sra-toolkit  see Tab. Raw reaA prospective case-control study from Huang et al., article  , GtRNAdb, piRNA database, Ensembl and Rfam). The only parameter used in command was -i for the path to the input file; -g, -m, -r, -t, -w for paths to genome, miRNA, rRNA, tRNA and piRNA files; -p for the number of involved processors. A summary text file was used as the list of gene counts for each sample.The sRNAnalyzer was launched with implemented databases “small RNA Databases” and “Human and Exogenous Databases”. “NCBI Non-Human Databases” was not used, as it was redundant, and all samples were pre-filtered before starting sRNAnalyzer. The only modified file was DB_cofig.conf. Pipeline_config.yaml file was not modified. As output, “XX.feature” file with summary counts for each sample was used after analysis.Transcript filtering based on expression values is an important step in DE analysis, as sRNA transcripts with a low coverage and low expression may add noise to the signal, thus decreasing the robustness of the analysis.Min filtering: expression value for a transcript in all samples is higher than the threshold (N)min(counts) > (N);Mean filtering: mean expression value for a transcript in all samples is higher than the threshold (N)mean(counts) > N;Median filtering: median expression value for a transcript in all samples is higher than the threshold (N)median(counts) > N.Three filtering strategies were applied:Two threshold values, N = 5 and N = 10, were applied.p-value < 0.05;p-value < 0.05.Adjusted Transcripts from the counts matrix were filtered using several thresholds: min count > {5,10}, mean count > {5,10}, median count > {5,10}. For DE analysis, data filtered by mean and median count > 5 were used. DE analysis was conducted using DESeq2 , edgeR  and limmp-value threshold (preferable);Adjusted p-value and absolute fold change thresholds.Combination of DE results in an expression signature, i.e., a list of transcripts with significant differences in expression between considered conditions. The following options are usually used for thresholds in the processing of the initial signature:p-value. Although this implies insufficient statistical power in the analysis, the results may suggest candidate transcripts for further exploration.Multiple testing correction is usually used in DE analysis. However, due to the low signal in sRNA expression data, many studies  only use the unadjusted The number of DE transcripts cannot be used as a robust metric for DE analysis quality, as this approach does not account for false-positive results. To evaluate the expression signature quality, we applied the Hobotnica  approachIn this process, the candidate expression signatures delivered after the application of differing preprocessing and processing procedures, such as a differential model or genome aligner, were inferred to the expression data to produce a distance matrix between samples. Next, the distance matrix was compared with the expected samples’ relationship structure, given the known groups of samples. The results of expression signature quality evaluation, presented as H-score value, reveal of the quality of a candidate signature’s data separation. H-score can be scaled from 0 (the worst) to 1 (the best).We used metrics for each stage of RNA-seq data analysis, from the biosampling and library preparation to the DE. These are described in Different post-DE downstream applications may be applied to the retrieved expression signatures. These include the Connectivity Map ,73, GO tIn this study, the effect of various factors that impact the expression analysis of human sRNA at different stages of data processing were investigated. The optimal pipeline alternatives and their parameters were identified, and an optimized pipeline for setting and running sRNA expression analysis was proposed. Assessing the resulting expression signatures with rank-statistics-based inference suggests a way to estimate the quality of resulting signatures and performance of bioinformatical analysis for particular biological data.


Falsified drugs are of serious concern to public health worldwide, particularly for developing countries where quality control of drugs is inefficient. In law enforcement against such fake medicines, there is a need to develop reliable, fast, and inexpensive screening methods. In this work, the ability of an innovative low-cost handheld near-infrared spectrometer to identify falsifications among two antimalarial fixed dose combination tablets, dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine, has been investigated. Analyzed samples were collected in Burkina Faso mainly in rural transborder areas that could be infiltrated by illicit drugs. A principal component analysis was applied on the acquired near-infrared spectra to identify trends, similarities, and differences between collected samples. This allowed to detect some samples of dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine which seemed to be falsified. These suspicious samples were semiquantitatively analyzed by thin-layer chromatography using Minalab® kits. Obtained results allowed to confirm the falsifications since the suspected samples did not contain any of the expected active pharmaceutical ingredients. The capacity of the low-cost near-infrared device to identify specifically a brand name of dihydroartemisinin/piperaquine or sulfadoxine/pyrimethamine has been also studied using soft independent modelling of class analogy (SIMCA) in the classical and data driven versions. The built models allowed a clear brand identification with 100% of both sensitivity and specificity in the studied cases. All these results demonstrate the potential of these low-cost near-infrared spectrometers to be used as first line screening tools, particularly in resource limited laboratories, for the detection of falsified antimalarial drugs. According to the World Health Organization (WHO), falsified medicines are defined as products that deliberately or fraudulently misrepresent their identity, composition, or source . Fake me®, based on semiquantitative thin-layer chromatography allowing the detection of falsified and grossly substandard drugs [® kit is less expensive and easy to implement, it is destructive, requires sample preparation steps, and uses reagents which are sometimes harmful and of environmental concerns.Drug quality control is a key issue in the supply chain monitoring, law enforcement, and ensuring patient protection . It is grd drugs , 10, 11.Near-infrared spectroscopy is an analytical technique well known for its potential in the detection of falsified medicines , 13. It Recently, some innovative handheld and low-cost NIR spectrophotometers have been commercialized. These low-cost devices are very compact and can operate autonomously using batteries . They ar® kits to confirm falsifications. The potential of the NIR spectrometer to identify specifically a brand name of DP or SP using soft independent modelling of class analogy (SIMCA) in the classical and data driven versions has been also investigated.We report in this study the ability of a low-cost NIR spectrometer as a screening tool to identify falsifications among two fixed dose antimalarial combination tablets, dihydroartemisinin/piperaquine (DP), and sulfadoxine/pyrimethamine (SP). Samples from different brands were collected in Burkina Faso mainly in rural transborder areas that could be infiltrated by medicines trafficking. A principal component analysis (PCA) was first applied as an exploratory tool on the acquired spectra to identify trends, similarities, and differences between collected samples and detect suspicious falsified samples. The suspicious samples were then analyzed using MinalabDihydroartemisinin-piperaquine (DP) and sulfadoxine-pyrimethamine (SP) tablets, both in fixed dose combination, were collected in Burkina Faso in different transborder zones . All sam® kits® and protocols [After the NIR analysis, samples were further semiquantitatively analyzed by thin-layer chromatography (TLC) using the Global Pharma Health Fund (GPHF) MiniLabrotocols . The refAnalyzes were performed using NIR-S-G1 spectrophotometer from InnoSpectra . It is a low-cost (less than 1000 €) handheld dispersive near-infrared instrument which can operate autonomously using batteries . The NIRTablet samples were directly scanned through their transparent blister, except for Maloxine® and Fansidar® samples. For these latter, spectra were directly recorded on the bare tablets because of the opacity of their primary packaging. Spectra of ten tablets per batch were recorded in the 900–1700 nm region for each formulation. Therefore, a total of 230 spectra was acquired.Prior to chemometric analysis, appropriate pretreatments of the acquired near-infrared spectra were necessary to eliminate irrelevant information which are mainly due to differences in physical characteristics of the samples. Therefore, data preprocessing was used to improve signal-to-noise ratio. The preprocessing consisted of a Savitzky-Golay smoothing and differentiation filter  followed by a multiplicative scatter correction (MSC). The chemometric analysis was performed on the spectral range between 1085 nm and 1601 nm because the other spectral areas were found noisy and less repeatable.Principal component analysis (PCA) is a common unsupervised technique which forms the basis for multivariate data analysis . It alloA classification method was built to evaluate the ability of the low-cost instrument to authenticate specifically a drug brand name. Chemometric models based on a class modelling or one-class classifier like soft independent modelling of class analogy (SIMCA) are more recommended for authentication purpose , 26. The(1) Classical SIMCA Analysis. The classical SIMCA algorithm uses samples with known origin (training samples) to perform a classification rule which allows classifying new samples (test samples) in one of the classes [Q and the Hotelling T2 statistics from the calibration data. The classification of a sample is based on the Q and T2 for the sample and the estimation of the T2 and Q distributions from the training data. This allowed to compute confidence limits set at 95%. With the PLS Toolbox software, these confidence limits are used to calculate the probability of a sample to be in a given class. A sample is attributed to a class if the probability is greater than a specified threshold probability value fixed at 0, 8 in this work. SIMCA models were built for each product. For Duo-Cotecxin® and Maloxine® products for which at least five batches have been collected, spectra of three batches were used as a training set for the model building and the two remaining batches were used as a test set. For the products for which only two batches were collected, Kennard-Stone algorithm was used to split the collected spectra into training (60 percent of spectra) and test (remaining 40 percent of spectra) sets for each product. Falsified samples were integrated only to the test set. classes . The dif(2) DD-SIMCA Analysis. SIMCA in its data driven version was also used to build a classification model for 2 target classes: Duo-Cotecxin® and Maloxine® products for which falsified samples have been identified and enough batches were collected. Like any SIMCA model, DD-SIMCA decomposes the training spectra of the target class by PCA [hi) and an orthogonal distance (vi) for each training sample [Q or Hotelling T2 statistics used in the original SIMCA models [α. The DD-SIMCA models are usually shown using a two-dimensional plot with a limit curve allowing to determine whether or not the samples belong to the target class [s by PCA , 26, 27.g sample , 28. EacA models . The calet class . For eacThe performance of the classical and data driven versions of SIMCA modelling was assessed based on sensitivity and specificity. Sensitivity is related to the percentage of samples from the target class that are correctly attributed as a member of the target class. Specificity is related to the percentage of samples from nonmembers of the target class, which are properly attributed as nonmembers of the target class .The spectral preprocessing, the PCA, and the classical SIMCA modelling were carried out using the PLS_Toolbox version 8.9.2, and while the DD-SIMCA analysis was done using DDSGUI, a graphical user interface freely available online . All cheA PCA was carried out on the acquired and pretreated spectra to enhance differences and similarities between the spectra and to identify underlying clusters . The PCAThe PC1-PC2 score plot, presented in The PCA applied on both SP and DP products allowed to see that DP spectra were also grouped, except some spectra of Duo-Cotecxin® which were far from other spectra of Duo-Cotecxin® and other DP medicines . These iThe PCA applied separately on SP and DP products  allowed ® kits which allow a rapid drug quality verification through a semiquantitative thin-layer chromatography [® kits are reliable to detect grossly substandard (less than 80% of the expected amount) or wrong drug samples [® tests and appeared to contain at least 80% of the expected amount of the respective active pharmaceutical ingredients.To confirm NIR results, all SP and DP tablets were analyzed using MiniLabtography . MiniLab samples . As expeThe falsification of Maloxine® samples was expected since they were purchased with illicit street vendor and their packaging was different of the packaging of licit Maloxine® samples. On the contrary, the falsified Duo-Cotecxin® sample was purchased in a licit drugstore and the visual inspection did not allow to notice a significant difference in comparison with the other Duo-Cotecxin® samples. These results allowed to demonstrate the great potential of the low-cost NIR spectrometer as a screening tool for the detection of falsified drugs without API.Considering PCA results, a classification method was investigated to evaluate the potential of the low-cost instrument to authenticate specifically a given brand name of SP and DP formulation. A class modelling method like SIMCA which is recommended for the verification of the identity of products , 26 has Original SIMCA models were constructed for each DP and SP product. Six SIMCA models were built and a correct classification rate of 100% of both sensitivity and specificity was obtained . FalsifiDD-SIMCA models were constructed only for Duo-Cotecxin® and Maloxine® formulations for which falsified samples have been identified and enough batches have been collected. Results of built DD-SIMCA models for the two target classes are shown in These results showed that despite the limited spectral range and low resolution of this low-cost spectrophotometer, it offers promising performance as a screening tool for proper falsification detection and specific brand identification of the antimalarial dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine drugs.The obtained results allow affirming that these innovative low-cost portable near-infrared spectrometers, associated to chemometric tools, offer promising performance to be used as an analytical method for routine testing against pharmaceutical falsification of antimalarial dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine drugs in their intact form. Despite their limited spectral range and low resolution, these devices allowed detecting falsified drugs with no active pharmaceutical ingredient and identifying specifically a brand name. This innovative handheld NIR spectrophotometer could be used as a first line screening tool in the detection and fight against antimalarial falsified drugs, particularly in developing countries. The implementation of such screening devices combined to a better monitoring of the medicine supply chain would reduce the infiltration of falsified drugs in licit drugstores.


Experimental results demonstrated that the XGBoost framework not only can reduce the cost of clinical cancer traceability but also has high efficiency, which might be useful in clinical usage.Cancer of unknown primary site (CUP) is a heterogeneous group of cancers whose tissue of origin remains unknown after detailed investigation by conventional clinical methods. The number of CUP accounts for roughly 3%–5% of all human malignancies. CUP patients are usually treated with broad-spectrum chemotherapy, which often leads to a poor prognosis. Recent studies suggest that the treatment targeting the primary lesion of CUP will significantly improve the prognosis of the patient. Therefore, it is urgent to develop an efficient method to accurately detect tissue of origin of CUP in clinical cancer research. In this work, we developed a novel framework that uses Extreme Gradient Boosting (XGBoost) to trace the primary site of CUP based on microarray-based gene expression data. First, we downloaded the microarray-based gene expression profiles of 59,385 genes for 57,08 samples from The Cancer Genome Atlas (TCGA) and 6,364 genes for 3,101 samples from the Gene Expression Omnibus (GEO). Both data were divided into training and independent testing data with a ratio of 4:1. Then, we obtained in the training data 200 and 290 genes from TCGA and the GEO datasets, respectively, to train XGBoost models for the identification of the primary site of CUP. The overall 5-fold cross-validation accuracies of our methods were 96.9% and 95.3% on TCGA and GEO training datasets, respectively. Meanwhile, the  Cancer of unknown primary site (CUP) is a rare type of tumor whose primary lesion cannot be determined even after a detailed investigation by conventional clinical medical methods . CUP onlRecently, next-generation sequencing technologies have facilitated the usage of biomarker-based personalized CUP therapies . With thMicroarray data analysis of gene expression files is a high-throughput sequencing approach using sequencing technology –25. SincThe framework of this study is shown in https://github.com/liqianyue/zeitgeist/tree/main/CUP/dataset.Microarray gene expression datasets of different cancer types were downloaded from TCGA and GEO. The detailed information of the datasets is summarized in The training and testing datasets were constructed as follows:(1) T dataset: Since the numbers of samples in each cancer vary widely, stratified random sampling was used to split the data into the training set and testing set . For bre(2) G dataset: By a similar rationale, stratified random sampling was also used to split the data. The ratios for breast cancer and colorectal cancer were set to 1:1, and those for other cancer types were 4:1. Finally, 1,853 samples were obtained across 10 cancers in the training set and 1,248 samples in the testing set.k nearest minority class neighbors. The synthetic instance is then created by choosing one of the k nearest neighbors b at random and connecting a and b to form a line segment in the feature space.It is worth noting that there is a large difference in the sample number across different cancer types in the training set, which will bias the prediction model. So the Synthetic Minority Oversampling Technique (SMOTE) method was chosen to balance the datasets, which is an oversampling method . SpecifiX  significant genes were sequentially selected as the input features of models, and then the performance of the model with these X-dimensional features in 5-fold cross-validation was recorded  were aggregated and used to measure the prediction performance of the model.P), recall (R), accuracy (ACC), and F1_score were usually adopted to assess the performance of the method. They have been widely used as measurement metrics in previous works , , …,  are the precision and recall calculated on the confusion matrix of each class separately. The average of pT, nT, pF, and nF are obtained by averaging the individual elements of the confusion matrix for all classes, they are recorded respectively as where (TPR) and false-positive rate (FPR) as the horizontal axis and the vertical axis, respectively. In addition, we were interested in the area under the ROC curve, denoted by AUC, which is another commonly used evaluation criterion. The TPR and FPR are defined in the following equations.To better measure the classification results of all cancer types, the receiver operating characteristic (ROC) was also drawn, which used the true-positive rate  is a protein-coding gene associated with cell cycle, mitosis, and meiosis. Papasavvas et al. found that SMC1B is a feature of cancer precursor dysplasia within high-risk HPV infection  and Kyoto Encyclopedia of Genes and Genomes (KEGG) are shown in R2-score of 96.38% based on XGBoost classification in the RNA-seq datasets of TCGA and the GEO. Liang used the 10-fold cross-validation to evaluate the overall accuracy of naive Bayesian algorithms, which reached 91%. Currently, the prediction for CUP was between 80% and 95%. In 2019, Albaradei with colleagues also proposed a deep learning model called Deep2Met to predict metastatic colorectal cancer using DNA methylation data, which achieved AUC and average F-scores of 0.97 and 0.95, respectively .The authors GT and QYL are employed by Genesis (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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Gene expression patterns among cancer types in the TMA (EdgeSeq) and TCGA (RNA-seq) were similar. EdgeSeq gene signature analyses aligned with the majority of pathological cancer types/subtypes and identified cancer-specific gene expression patterns. TMA IHC H-scores for HER3 varied across cancer types/subtypes. In a few cancer types, HER3 mRNA and protein expression did not align, including lower liver hepatocellular carcinoma IHC H-score, compared with mRNA. Although all TNBC and ovarian cancer subtypes expressed mRNA, some had lower protein expression. This was seen in TMA and TCGA data sets, but not in CCLE. The EdgeSeq TMA data set can expand upon current biomarker data by including cancers not currently in TCGA. The primary analysis of EdgeSeq and IHC comparison suggested a unique protein-level regulation of HER3 in some tumor subtypes and highlights the importance of investigating protein levels of ADC targets in both tumor and normal tissues.Molecular and protein biomarker profiling are key to oncology drug development. Antibody-drug conjugates (ADCs) directly deliver chemotherapeutic agents into tumor cells based on unique cancer cell biomarkers. A pan-cancer tissue microarray (TMA) data set and gene panel were validated and gene signature analyses were conducted on normal and cancer tissues to refine selection of ADC targets. Correlation of mRNA and protein levels, and human epidermal growth factor receptor (HER) expression patterns were assessed. An EdgeSeq biomarker panel (2862 genes) was used across 8531 samples  with an established TMA data set, and immune cell and cell cycle gene signatures were analyzed. Discriminating gene expression signatures were defined based on pathological classification of cancer subtypes. Correlative analyses of HER2 and HER3 mRNA (EdgeSeq) and protein expression (immunohistochemistry  EGFR, HER2, HER3) ) were calculated and used for the analyses. An empirical parameter was set to reduce data of low-quality samples from the final data set using house-keeping gene expression levels. Correlations (R) between each sample data and an average of universal RNA (uRNA) gene expression levels were calculated from 89 samples that passed the sequencing filter. Samples with low correlations (R < 0.4) were considered low quality and a sample quality filter was applied by empirically setting an exclusion parameter based on average of expression values of nine housekeeping genes (Supporting Information). Through these processes, we obtained 2479 data samples  for 2867 probes. For the described analyses, five probes/genes among 2867 were not used for comparison of the EdgeSeq data with the Human Genome build 38 (hg38) data set used by TCGA and in-house RNA-seq (Supporting Information).Quality filters were applied to exclude the following samples with <1.5 million total read count or <0.1 of relative standard deviation of all probes (Supporting Information). Background signal from negative controls  was subtracted from all gene probes as well as undergoing upper-quartile (UQ) normalization to reduce tissue-specific bias of the expression data (Supporting Information). Log transformed values (loghttps://xenabrowser.net/). Some probes were not used in the comparison of EdgeSeq with TCGA or with in-house RNA-seq data because of circumstances described in Supporting Information. Comparisons were based on gene symbol with Ensemble Gene-ID. Gene annotations for cell type gene signature analysis were provided by HTG and public data  (RNA-seq data for TCGA were downloaded from UCSC-Xena (9198524)  Files. Z9198524) –S7 FilesCancer cell lines  were har2 [TPM-UQ + 1]) and EdgeSeq data (log2 [adjCPM-UQ + 1]) were calculated.For RNA-seq analysis, mRNA was purified from 1 μg of total RNA using NEBNext Poly(A) mRNA Magnetic Isolation Module . Complementary DNA (cDNA) was generated using NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs) (Supporting Information). EdgeSeq raw data (94 cell line samples) were processed in the same manner as TMA data  and correlations, slopes and intercepts between RNA-seq data (logR > 0.4 and approaching 1.0) for most genes, including HER2, HER3 and PD-L1 , progesterone receptor-positive (PR+), HER2− breast cancer (ER+/PR+/HER2−), while generally lower in normal tissues , endometrioid (n = 34), mucinous (n = 14) and serous (n = 110); the first three are not a component of TCGA. Genes selectively over- or under-expressed in each subtype among the ovarian cancer samples were assessed (CEACAM6 (mucinous), DKK1 (endometrioid), HNF1B (clear cell and mucinous), PGR (endometrioid), TFF3 (mucinous) and WT1 (serous) , adenoid cystic carcinoma [Ad], basal cell adenoma [Ba] and mucoepidermoid carcinoma [Mu]), benign epithelial tumor subtypes (pleomorphic adenoma [Pl] and warthin tumor [Wa]) and normal salivary gland tissue (n = 10). Marker genes for each subtype were selected based on higher and lower expression over other subtypes and normal salivary gland tissues. Analysis suggested overall correspondence between pathological and gene signature subtypes for salivary gland cancer  and cell cycle-related genes . These patterns (complementary profiles in Ad and Bl and a unique Mu profile) were reflected in principal component analysis , housekeeping genes, cell cycle genes and CAFs were determined for TNBC and salivary gland cancer subtypes . For TNBHER gene family member expression in salivary gland cancer showed that Mu and Wa subtypes had higher EGFR expression among the salivary gland cancer subtypes  and CCLE (R = 0.834 and R = 0.844)  .HER3 mRNA (HER3 EdgeSeq mRNA level (Comparison of HER2 mRNA (EdgeSeq) and protein levels by H-score  were highest in the serous subtype  and in vivo (TMA) data, as well as those between mRNA and protein expression. In TMA and TCGA data sets, expression levels of HER3 mRNA/protein in TNBC were higher in LAR and M than BL1 and BL2, with similar levels as ER+/PR+/HER2− and HER2+ breast cancer subtypes. However, the same expression patterns in TNBC were not observed in the CCLE data set  Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(XLSX)Click here for additional data file.S7 File(XLSX)Click here for additional data file.S8 File(DOCX)Click here for additional data file.


A total of 25 differentially expressed proteins (DEPs) overlapped in proteomic studies of urine and blood samples. GO analysis showed that the DEPs were mainly involved in the immune system and blood coagulation. Pathway analysis showed that the complement and coagulation cascade pathways were well enriched. We found that immunoglobulin heavy constant alpha 1 (IGHA1) and immunoglobulin κ constant (IGKC) showed good performance in distinguishing AR from non-rejection groups validated with several GEO datasets. Through RNA sequencing, the combination of IGHA1, IGKC, glomerular filtration rate, and donor age showed good performance in the diagnosis of AR with ROC AUC 91.4% (95% CI: 82–100%). Our findings may contribute to the discovery of potential biomarkers for AR monitoring.A kidney transplant is often the best treatment for end-stage renal disease. Although immunosuppressive therapy sharply reduces the occurrence of acute allograft rejection (AR), it remains the main cause of allograft dysfunction. We aimed to identify effective biomarkers for AR instead of invasive kidney transplant biopsy. We integrated the results of several proteomics studies related to AR and utilized public data sources. Gene ontology (GO) and pathway analyses were used to identify important biological processes and pathways. The performance of the identified proteins was validated using several public gene expression omnibus (GEO) datasets. Samples that performed well were selected for further validation through RNA sequencing of peripheral blood mononuclear cells of patients with AR ( Renal transplantation is considered the best treatment for patients with end-stage renal disease (ESRD), which leads to a higher survival rate and a better quality of life . AlthougRapid advancements in genomics, transcriptomics, and proteomics technologies have promoted their application in understanding graft injury mechanisms. They have revealed promising biomarkers for reflecting the underlying biological processes . The genIn recent years, many researchers have uploaded experimental data to public databases for validation or further use by other scientific researchers. Therefore, public databases have become an advantageous resource in disease research, especially for the discovery and verification of biomarkers. They have saved valuable time and resources for subsequent researchers. There are already some good examples showing that mining public databases is an effective and practical method for optimizing studies .In this study, we acquired differentially expressed proteins (DEPs) in urine and blood samples from proteomic studies related to acute renal allograft rejection. Gene ontology (GO) and pathway analysis of the DEPs revealed that some biological processes were associated with the immune system and probably played important roles in acute allograft rejection. We then used public datasets to identify potential biomarkers for the diagnosis of AR and non-rejection groups. For further validation, we performed RNA sequencing of peripheral blood mononuclear cells (PBMCs) of patients with AR and in the non-rejection groups. The results showed that immunoglobulin κ constant (IGKC) region and immunoglobulin heavy constant alpha 1 (IGHA1) had good performance in distinguishing AR from non-rejection. The insights of this study may help deepen the understanding of the mechanism of acute allograft rejection and identify potential biomarkers for further characterization.We included 12 proteomic studies related to renal allograft rejection from 2011 to 2021, including six used urine samples and six used blood samples . For fur1 were applied to GO analysis and pathway analysis, which supply a significant set of functional annotations to investigators to better understand the biological significance of DEPs. The GO terms included cellular component (CC), molecular function (MF), and biological process (BP). Significant pathways and GO items were defined for an adjusted p-value of < 0.05.Robust DAVID tools2 is a robust database that integrates many publicly available kidney gene expression profiles. It provides researchers with gene expression data for data mining and visualization. Additionally, it uses different expression data for meta-analysis, a function that has been widely used in recent years  were used to calculate the expression of candidate biomarkers in human samples. Among different samples, the receiver operating characteristic (ROC) curve was applied to evaluate the performance of biomarkers in distinguishing AR from non-rejection groups.Human microarray data were downloaded from the GEO database.Thirty-five ESRD patients who underwent kidney transplantation at the Kidney Disease Center of the First Affiliated Hospital of Zhejiang University were included. Patient demographics are shown in After integrating the reported DEPs between AR and non-rejection samples, 158 DEPs and 119 DEPs were identified from the blood and urine samples, respectively. A total of 25 DEPs overlapped between the two groups . To idenDEPs from urine and blood samples were further analyzed using DAVID tools. As shown in Staphylococcus aureus infection. We found that the complement and coagulation cascade pathways were enriched both in the DEPs of urine samples and blood samples, as well as in the overlapped DEPs, suggesting that this pathway probably played an important role in the occurrence of acute allograft rejection.As shown in p-value < 0.05 and a fold change > 1.5. Similar results were also observed in patients with AR and with normal kidney tissues. Since IGHA1, IGKC, B2M, and CFB play important roles in the immune system, we further validated the four biomarkers in other public datasets. As shown in According to the meta-analysis of Nephroseq, 25 DEPs were uploaded to the website to determine whether they were differentially expressed in patients with AR. As shown in The demographic and clinical characteristics of the patients are presented in t-tests and chi-squared test. P-values < 0.05 were considered significant. All data were statistically analyzed using SPSS Statistics V20 , and statistical charts were created using GraphPad Prism software .The results are presented as the means ± SD for continuous variables. Categorical variables were expressed in terms of rate (%) or composition ratio (%). For statistical comparisons of the clinical data between two groups, we used unpaired, two-tailed In recent years, the effective use of immunosuppressive drugs has sharply reduced the rate of renal graft rejection; however, AR still occurs in approximately 10–20% of total reported cases. It has been reported that every episode of rejection is closely associated with a poor graft survival rate, even after immunosuppressive treatment , 12. TheIn the past decade, proteomics has been widely used in scientific research, especially for the discovery of effective biomarkers for various diseases. Many biomarkers detected by proteomic methods have shown good accuracy and sensitivity in the diagnosis of various types of rejection –16; howeThrough GO and pathway analyses, we found common features shared by urine and blood samples, including B cell activation, phagocytosis, immune response, and platelet degranulation. These biological processes are involved in the immune system and play an important role in acute allograft rejection. It is well known that the immune system is closely related to AR, which can occur any time after allograft transplantation, including antibody-mediated rejection and acute T cell-mediated rejection. Both of these rejections include the detected biological processes. As for pathway analysis, complement and coagulation cascade pathways were found to be enriched in DEPs from urine and blood samples, suggesting that it is a key pathway that mediates the process of acute allograft rejection.In our analysis, we found four proteins that were significantly increased in the AR groups compared to both non-rejection groups and normal kidney tissues . To valiIGKC is an important component of immunoglobulins, which are involved in innate and adaptive immunity, and is an effective prognostic biomarker for breast cancer and non-small cell lung carcinoma (NSCLC) . SchmidtIGHA1 is an adaptive immune effector that is a highly abundant circulating protein with relatively stable blood concentrations . It was As shown in Our study had few limitations. First, in the process of identifying DEPs, we were unable to acquire whole raw data from the included studies for reanalysis, and the methods used to define DEPs were different among the studies, which might have influenced the results. Second, the sample size was relatively small, and more studies are needed to validate our findings. Third, recipients usually receive immunosuppressive treatments before transplantation, which may have an unaddressed effect on our results. Therefore, these limitations should be considered when testing the clinical utility of identified biomarkers in blinded prospective studies.In summary, our study integrated the results of several proteomic studies and utilized public data sources to identify proteins of interest related to acute allograft rejection. Through bioinformatic analysis, we identified some important biological processes and pathways that probably play an important role in AR. Finally, we performed RNA-Seq of PBMCs obtained from patients in our study and found that the combination of IGHA1, IGKC, GFR, and donor age showed good performance with an ROC AUC of 91.4% (95% CI: 82–100%). An in-depth integrated analysis of proteomics and transcriptomic data revealed that IGHA1, IGKC, GFR, and donor age had potential for clinical use in monitoring transplant patients.OMIX001115 (https://ngdc.cncb.ac.cn/omix/release/OMIX001115).The data presented in the study are deposited in the China National Center for Bioinformation repository, accession number This study was approved by the Institutional Review Board of the Zhejiang University School of Medicine. The patients/participants provided their written informed consent to participate in this study.SH participated in the writing of the manuscript, performance of the research, and data analysis. WZ participated during the research with patients. CW participated in data analysis and in the writing of the manuscript. YW contributed in the research design. RS participated in data analysis. HH contributed analytic tools and data analysis. HJ contributed in the research design and in the writing of the manuscript. JC contributed in the research design and in the writing and revision of the manuscript. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.