Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development. Cancer is a complex and highly individualized disease with diverse subtypes, and molecular heterogeneity exists among different subtypes of the same cancer type . As cancHigh-throughput sequencing technologies generate a large amount of multi-omics data , which pWith the strong representation ability of graph-structured data, graph neural networks (GNNs) have achieved great success and are gradually used in a node classification task. It provides one way to obtain new representations of nodes by combining the connectivity and features of its local neighborhood. Recently, some GNN-based methods have been proposed to predict molecular subtyping of cancer. Rhee et al. developed a GCN-based model to explore the gene–gene association and information passing for cancer subtyping . Lee et It is well known that abnormal behaviors of cancer cells are the result of a series of gene mutations, gene copy number variation, and gene transcription level changes in key regulatory pathways . A singlIn this study, we propose a novel and general framework M-GCN  for moleWe collect gene expression, SNV and CNV data, and clinical information of breast cancer (BRCA) and stomach adenocarcinoma (STAD) patients from The Cancer Genome Atlas (TCGA) database . As showr (TNBC) , and STAr (TNBC) , respectGenes whose expression values are lower than 10 and 3 are considered as not expressed in BRCA and STAD and then deleted, respectively. As RNA-seq data of these two cancer types are obtained from different platforms in TCGA, we set different cutoffs according to their data distributions. Then, fragments per kilobase of exon per million fragments mapped (FPKM) values of gene expression are normalized with log2-transformation. In each cancer type, a gene’s mutation frequency is defined as the number of samples with this mutation divided by the total number of samples. Genes with mutation frequency greater than To obtain molecular subtype-related transcriptome features with low noise for constructing a purified sample–sample similarity graph and effective message passing, we apply a supervised non-linear feature selection method HSIC Lasso , which c1n is the Let Since samples with similar features are more likely to fall into the same category, we first construct a sample–sample graph based on similarity of each samples pair. From the perspective of the whole biological system, compared with SNV and CNV, gene expression is the most fundamental level at which the genotype gives rise to the phenotype. So we only use transcriptomic data and apply spearman’s correlation to calculate the similarity of each two samples. We select sample pairs whose correlation coefficient  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis on top 40 subtype-specific genes. The R package “clusterProfiler” is used. K-nearest neighbor classifier (KNN), random forest (RF), support vector machine classifier (SVM), and Gaussian naive Bayes (GNB) are traditional machine learning methods and we utilize gene expression, SNV, and CNV data as features. DeepCC is a neural network-based method which utilizes transcriptomic data and leverages feedforward neural networks to classify molecular subtypes. Li’s method is a GCN-based molecular subtyping method which integrates CNV data and gene expression data.To demonstrate the performance of our method, we compare the performance of M-GCN with six commonly used or advanced methods on STAD and BRCA molecular subtyping including traditional machine learning–based methods, neural network–based method, and a GCN-based method:For BRCA, our framework M-GCN achieves best performance . M-GCN aFurthermore, we analyze the detailed results of subtype classification. As shown in For the subtype classification task with more classes and smaller sample size, our method still performs best on STAD than other methods in all metrics . The perlidation , M-GCN hOur framework M-GCN achieves best performance in BRCA and STAD molecular subtypes. The number of samples in BRCA is greater than that in STAD, and all of these methods in BRCA have good accuracy. The machine learning–based methods such as RF, SVM, and GNB have a significant difference between BRCA and STAD tasks. In addition, these methods in traditional machine learning–based methods are shallow and cannot learn the deep and complex representations of sample nodes. The performance of the neural network–based method DeepCC is higher than most of these machine learning–based methods, which shows the deep and non-linear representation are important. Li’s method may suitable for the task with more samples. M-GCN still has the better scores in evaluation metrics than the multiple omic-based methods by utilizing the cleaned structure information and message passing of sample nodes.After assessing the performance compared with other methods, we conduct three ablation experiments to evaluate the contributions of feature selection step, SNV data, and CNV data in STAD, respectively . The basOverall, the results of ablation experiments in STAD demonstrate that feature selection, SNV data, and CNV data are essential. Especially, feature selection makes a more significant contribution. One possible reason for this is that selected subtype-related features can help learn good representations of sample and reduce noise of the sample–sample graph.Similarly, to explore contributions of feature selection, SNV, and CNV data for molecular subtyping of BRCA, we also perform ablation experiments. The results of three ablation experiments are shown in On the basis of selected transcriptomic features that could accurately classify the breast cancer samples into various molecular subtypes, we further obtained the subtype-specific genes.We identify ten genes with highest specificity score of each subtype, the gene lists are shown in Moreover, specific genes of ER+, HER2+, and TNBC are significantly enriched in biological processes of response to estradiol, ERBB2 signaling pathway, and sequestering of actin monomers, respectively. Some of the findings are also highly consistent with current understandings. Daniel et al. have found that estrogen were important drivers of breast cancer proliferation and PR-B expression increased breast cancer cell growth in response to estradiol . Shah etCompared with BRCA, the current understanding of subtype markers and biological mechanisms of STAD is much less and our analysis is expected to provide more insight. From the gene expression heatmap across all the samples, it can be concluded that biomarkers of STAD perform well in distinguishing EBV, GS, and MSI . ThroughThe generation of large amounts of multi-omics data and development of deep learning methods offer a more effective mean to study the personalized diagnosis and treatment options of complex diseases, such as cancer . In thisAblation experiments demonstrate that subtype-dependent feature selection contributes most to the improvement of classification performance of cancer molecular subtypes. Furthermore, we verify the stability of the feature selection process to ensure that obtained features are reliable. When shuffling the samples and using 90% of them to perform feature selection, we find the intersection of features picked out by the 10 rounds of feature selection processes are very large for both BRCA and STAD. This is extremely beneficial to train a stable GCN-based model.On the basis of subtype-related features, we further identify a few subtype-specific features which can potentially be used for diagnostic biomarkers. In our study, ESR1, ERBB2, and MFI2 are predicted as the subtype-specific biomarkers because they have the highest specificity scores for ER+, HER2+, and TNBC samples, respectively. It is worth noting that ESR1 and ERBB2 are well accepted markers for ER+ and HER2+ breast subtype, indicating our prediction is highly consistent with current understanding. Although MFI2 has not been demonstrated as biomarkers of TNBC subtype by wet lab and clinical experiments, its encoding protein shares sequence similarity and iron-binding properties with members of the transferrin superfamily. Public studies have demonstrated these iron-binding properties serve iron uptake and promote cell proliferation, and high expression of these proteins are associated with the decreased overall survival of patients in many cancer types . As we kLarge amount of multi-omics data generated by rapid development of high-throughput technologies has enabled data-driven methods to apply in molecular subtyping of cancer. We proposed a robust GCN-based framework M-GCN for molecular subtyping of cancer by integrating gene expression, SNV, and CNV data. In addition to comprehensive information of individual samples, M-GCN fully considers message aggregation among samples for subtype classification. Compared with other six advanced computational methods, M-GCN achieves the best classification performance for molecular subtyping of breast and stomach cancer. Through ablation experiments, we demonstrate subtype-related transcriptomics features obtained by HSIC Lasso method highly contribute to sample classification, which is probably because the selected features eliminate data noise and facilitate the construction of purified graph. On the basis of the graph structure constructed by HSIC Lasso, M-GCN further strengthens connections between new features and the graph by assigning weights. By assigning higher weights, M-GCN aims to successfully pass message in GCN. Furthermore, the identified molecular subtype-specific marker of breast cancer is highly consistent with clinical cognition, so the predicted biomarkers of stomach cancer are promising to be used for molecular typing diagnosis of patients, filling in the current gap.


Conclusions: All groups administered enrofloxacin and oregano oil did not develop a drug-resistant phenotype during the 5-day treatment period, as grouped in this trial. In 2022, after Announcement No. 194 of the Ministry of Agriculture and Rural Affairs in China, the antimicrobial resistance (AMR) trend declined, but it did not fundamentally change, presumably due to the impact of environmental pollution caused by the long-term use of antimicrobials.Objectives: The goal of this study was to reveal the gut microbiota of pigs and chickens in central China and the dynamics of antibiotic resistance genes carried by microorganisms. Methods: Free range and factory-farmed Gushi chickens and Huainan pigs were divided into eight groups. Faecal samples were collected from each group, and the metagenomic sequencing method was used to detect each group of samples. Results: The resistance genes showed the following trend, from high to low relative abundance: tetW was the highest, followed by tetW/N/W, then lnuA; and others from high to low were mdtB, lnuC, ANT6-la, ErmB, mdtC, ErmQ, tetBP, vatE, evgS, acrB, cpxA, mefA, Escherichia coli-ampC, tetL, yojl, AcrF and mdtA. All groups administered enrofloxacin and oregano oil did not develop a drug-resistant phenotype during the 5-day treatment period, as grouped in this trial. In 2022, after Announcement No. 194 of the Ministry of Agriculture and Rural Affairs in China, the antimicrobial resistance (AMR) trend declined, but it did not fundamentally change, presumably due to the impact of environmental pollution caused by the long-term use of antimicrobials.Background: Basic data concerning the gut microbiota of the main animal husbandry breeds (pigs and chickens) are scarce in China. The dynamics of gut microbiota (pigs and chickens) in China and antibiotic resistance genes carried by microorganisms in the natural environment are unknown. Methods: Free range and factory-farmed Gushi chickens and Huainan pigs were divided into eight groups. Faecal samples were collected from each group, and the metagenomic sequencing method was used to detect each group of samples. Results: The resistance genes showed the following trend, from high to low relative abundance:  The discovery and widespread use of antimicrobials in the development of human civilization has an indelible meaning . The extHowever, in the long term, AMR has become a major threat to human health in the 21st century. The animal breeding industry is generally believed to be one of the main sources of AMR, which causes resistant bacteria and genes to circulate through the entire chain of animals, food, environment, and humans, and threatens human health. At present, AMR is common in bacteria of animal origin. Multidrug resistant and even pandrug resistant strains are constantly appearing ,14,15,16Irrational use of antimicrobials promotes the emergence of AMR, resulting in changes in the composition and diversity of animal gut microbiota, which adversely affect host health . QuantitThe study of microorganisms has been based mainly on pure culture for the hundreds of years since Antoni van Leeuwenhoek invented the microscope. Among the trillions of microbial species, only 0.1–1% of the species are culturable , which gMetagenomics is a method that was first proposed by Handelman  to studyIn recent years, with the rapid development of sequencing technology and information technology, the use of next-generation sequencing technology to study metagenomics can quickly and accurately obtain a large amount of biological data and rich microbial research information ,25,26. SAccording to the statistics of the World Food and Agriculture Organization, China’s number of live pigs, total pork production and poultry egg production ranks first in the world, and poultry egg production has ranked first in the world for many consecutive years. China has become one of the most important animal husbandry countries. The current understanding of the pig and chicken gut microbiome is limited, due mainly to the lack of comprehensive pig and chicken gut microbial reference gene sets and genome sets, which severely limits the mining and use of metagenomic sequencing data.Fluoroquinolones are important antimicrobials used to treat Salmonella infections in poultry. Studies have shown that high-dose enrofloxacin ) has a good killing effect on Salmonella enterica in chickens, but at the same time, the composition and structure of intestinal flora are greatly affected .E. coli wild-type population [The observed intestinal ENR concentrations were shown to be both theoretically (based on pharmacokinetic and pharmacodynamic principles) and effectively (in vivo measurement) capable of significantly reducing the intestinal pulation .In this study, the characteristics of drug resistance groups, drug-resistant bacteria and ARG contamination levels of pigs and chickens on different farms under different feeding modes were studied to provide a reference for the optimisation of antibiotic use in farms.In this study, ten fresh faeces of Gushi chickens were collected from an intensive farm in Gushi County, Henan Province, P.R. China, on 15 July 2022, and ten fresh faeces of Gushi chickens were collected from a family farm. Ten fresh faeces of Huainan pigs were collected from an intensive Huainan pig farm, and 10 fresh faeces of Huainan pigs were collected from a mountain farm. The above collected subjects had not used antimicrobials in the past 2 months. On 19 July 2022, 10 faeces of the factory-bred Gushi chicken oregano oil group, 10 faeces of the enrofloxacin group (treatment amount), and 10 faeces of the factory-bred Huainan pig oregano oil group and enrofloxacin group (treatment amount) were collected, respectively. All animal-related work was carried out in strict accordance with the “Administrative Regulations on Laboratory Animals” formulated by the Ministry of Agriculture and Rural Affairs of China, and the project was approved by the Animal Ethics and Welfare Committee (AEWC) of Xinyang Agriculture and Forestry University, and then transported to the laboratory for extraction of total nucleic acid from the samples. Total DNA and total RNA were extracted from 80 samples.A.Free-range Gushi chicken without an additive group;B.Factory-bred Gushi chicken oregano oil group (0.25 mL/kg BW);C.Factory-bred Gushi chicken ENR group (5.0 mg/kg BW);D.Free-range Huainan pigs without an additive group;E.Factory-bred Huainan pigs without an additive group;F.Factory-bred Huainan pigs (2021011803). All samples were stored in the sample preservation solution, mixed with pig oregano oil group (0.25 mL/kg BW);G.Factory-bred Huainan pigs ENR Group (5.0 mg/kg BW)H.Factory-bred Gushi chicken without an additive group.Considering the consistency of animal species, age, weight, feeding place, feed and drinking water, the following test scheme was adopted in order to reduce the impact of the test on animal production. The sample size of this experiment was sufficient to reflect the basic distribution situation of the intestinal flora of the animals.Factory or intensive breeding was performed by industrial production in a factory farm in the following manner. First, the scale of factory breeding was large . Second, the hybrid combination or mating line of animals with a consistent body shape, appearance, and balanced growth, were selected. Third, some mechanised and automatic equipment, such as automatic drinking fountains, automatic feeding troughs, and automatic dung cleaning, were used. Fourth, fewer staff, less land occupation, and high labor productivity, occurred. Fifth, scientific operation and management methods were used to organise production, so that production conditions and technological processes operated in accordance with standards and rules, including full price compound feed, modern epidemic prevention measures and all inflow and all outflow production process.The family farm, or mountain farm, operated under the free-range breeding model. The family farm or mountain farm engaged in animal husbandry with an annual production of more than 500 pigs, more than 5000 chickens for broilers, more than 2000 laying hens, or more than 3000 mixed breeding poultry. The animals moved freely. The animals were fed full price compound feed, and wild and green feed. Normal epidemic prevention and expulsion of parasites occurred.The above animals were all adult animals. Pigs were in the fattening stage (5–8 months), and chickens were in the laying stage (19–28 weeks). The ENR group and oregano oil group were administered oregano orally twice a day for 5 days. The water intake of the animals was controlled in the experiment.The raw samples or the extracted nucleic acid samples was transported cold  to the biotech company. The received samples were tested for eligibility. Qualified DNA samples were detected, and library construction and library detection were performed. Illumina NOVASEQ 6000  was used to sequence the qualified library, and the raw data obtained by sequencing were used for later information analysis.Library construction and sequencing yielded a sufficient amount of high-quality nucleic acids. The detection of DNA samples mainly included the analysis of DNA purity and integrity by agarose gel electrophoresis (AGE), and Qubit accurately quantified the DNA concentration.Qualified DNA samples were randomly broken into fragments of approximately 350 bp in length with a Covaris ultrasonic breaker, and the entire library was prepared through the steps of end repair, A-tail addition, sequencing adapter addition, purification, and PCR amplification.After the library was constructed, Qubit 2.0 was used for preliminary quantification, the library was diluted to 2 ng/µL, and then an Agilent 2100  was used to detect the insert size of the library. After the insert size met expectations, the Q-PCR method was used to determine the effective concentration of the library. Accurate quantification (library effective concentration > 3 nM) was performed to ensure library quality.After the library was qualified, the different libraries were pooled according to the requirements of effective concentration and target data volume, and then Illumina NOVASEQ 6000 sequencing was performed.There was a certain proportion of low-quality data in the raw data obtained by sequencing. To ensure the accuracy and reliability of subsequent information analysis results, quality control and host filtering of the raw data were first carried out to obtain valid data.Reads that contained low-quality bases  that exceeded a certain percentage (set to 40 bp by default) were removed.Reads with a certain proportion of N bases (default was 10 bp) were removed.Reads whose overlap with the adapter exceeded a certain threshold (set to 15 bp by default) were removed.Metagenome assembly started from clean data after the quality control of each sample was performed.After preprocessing, clean data were obtained, and MEGAHIT assembly software was used for assembly analysis.The assembly parameters were: presets meta-large.The assembled scaffolds were interrupted from N junctions to obtain sequence fragments without N, called scaftigs .After quality control, the clean data of each sample were compared with the assembled scaftigs of each sample using Bowtie2 Version 2.4.5 software.The alignment parameters were: –end-to-end, –sensitive, -I 200, -X 400.For scaftigs generated by single-sample assembly, fragments below 500 bp were filtered out, and statistical analysis and subsequent gene prediction were performed.Gene prediction started from scaftigs assembled from single samples. MetaGeneMark was used for gene prediction, and the genes predicted from each sample were combined to remove redundancy and construct a gene catalogue. Based on the clean data of each sample, the abundance information of the gene catalogue in each sample was obtained.The basic steps of gene prediction and abundance analysis were as follows.Starting from the scaftigs (≥500 bp) of each sample, MetaGeneMark was used to perform an open reading frame (ORF) prediction, and information of length less than 100 nt was filtered out.The open reading frame (ORF) prediction results assembled by each sample were made de-redundant using CD-HIT software to obtain a nonredundant initial gene catalogue . The default was identity 95%, coverage 90% for clustering, and the longest sequence was selected as the representative sequence.The parameters were: -c 0.95, -G 0, -aS 0.9, -g 1, -d 0.The clean data of each sample were compared with the initial gene catalogue by Bowtie2, and the number of reads of the gene in each sample was calculated.The alignment parameters were: –end-to-end, –sensitive, -I 200, -X 400.The genes that supported the number of reads ≤2 in each sample were filtered out, and the final gene catalogue (unigenes) for subsequent analysis was obtained.r is the number of reads of the gene in the alignment, and L is the length of the gene.Based on the number of reads and gene length in the alignment, the abundance information of each gene in each sample was calculated, and the calculation formula was as follows:Based on the abundance information of each gene in each sample in the gene catalogue, the basic information statistics, core–pan gene analysis, correlation analysis between samples, and Venn diagram analysis of the number of genes were performed.The species annotation information of each gene (Unigene) was obtained from the gene catalogue and compared with the MicroNR library, and the species abundance table of different taxonomic levels was obtained by combining the gene abundance table.Based on the species abundance table and functional abundance table, the abundance clustering analysis, PCA and NMDS dimensionality reduction analysis, ANOSIM analysis and sample clustering analysis were able to be performed. When group information was available, multivariate statistical analysis of Metastat and LEfSe and comparative analysis of metabolic pathways were also able to be performed to explore the differences in species composition and functional composition among samples.The gene catalogue and the Comprehensive Antibiotic Resistance Database (CARD) were used for annotation, the abundance distribution of resistance genes was obtained, and the species attribution and resistance mechanism of these resistance genes were found.The sequencing data preprocessing results are shown in Based on the assembly results, the distribution of the length of scaftigs in each sample was counted and plotted, and the results are shown in The gene catalogue length distribution is shown in Based on the abundance table of genes in each sample, the number of genes in each sample can be obtained. By randomly extracting different numbers of samples, the number of genes between combinations of different numbers of samples can be obtained. Therefore, we constructed and mapped the dilution curve of the core and pan genes, and the results are shown in To examine the number of genes between the specified groups, the coexistence and unique information between different groups were analysed, and a Venn graph, or petals, were drawn. The results are shown in Biological repetition is necessary for biological experiments, and high-throughput sequencing technology is no exception. The correlation between the genetic abundance of the sample is an important indicator for testing the reliability of the experiment and whether the sample is selected. The closer to the correlation coefficient, the higher the similarities of the gene abatement mode between the samples. The results are shown in −5).Genes were compared in various functional databases by DIAMOND software. Unigenes were compared with sequences for alignment p, e-value ≤ 1 × 10Alignment result filtering: for the alignment result of each sequence, the alignment result with evalue ≤ minimum evalue*10 was selected for subsequent analysis.After filtering, since each sequence may have multiple alignment results and obtain multiple different species classification, to ensure its biological significance, the lowest common ancestor (LCA) algorithm (applied to the systematic classification of MEGAN software) was applied.The taxonomic level before a branch represented the species annotation information of the sequence.Based on the LCA annotation results and gene abundance table, the abundance information of each sample at each taxonomic level  was obtained. The abundance of a species in a sample was equal to the sum of gene abundances that the species annotated as the species.Based on the LCA annotation results and the gene abundance table, the number of genes in each sample at each taxonomic level  was obtained. Among the genes of the species, the number of genes whose abundance was not 0 was presented.Based on the abundance table at each taxonomic level (Jiemenae genus and species) and Krona analysis, the relative abundance overview was displayed, the abundance cluster heatmap was displayed, PCA and nonmetric multidimensional scaling (NMDS) dimensionality reduction were analysed, the ANOSIM difference between (within) groups was analysed, and Metastat and LEfSe multivariate statistical analyses of different species between groups were performed.To comprehensively and intuitively display the relative abundance of different classification levels in each sample, we used Krona to display the species annotation results. The results are shown in Studies have shown that the gut microbiota is individual-specific, and the composition of the microbiota of pigs under different feeding conditions and different intestinal parts is different.Based on the relative abundance tables of different classification levels, the top 10 species with the largest relative abundance were selected in each group, the rest of the species were set as Others, and the species annotation results corresponding to each sample in the column chart of relative abundance at different taxonomic levels are shown in Based on the relative abundance tables of different classification levels, the top 35 genera in terms of abundance and their abundance information in each sample were selected to draw a heatmap, and were clustered at the species level to facilitate the display of results and information discovery to determine the species with more aggregation in the sample. The results are shown in At present, principal component analysis (PCA) and nonmetric multidimensional scaling (NMDS) are the main methods of dimension reduction analysis applied to ecological research. Among these methods, PCA is a dimension reduction analysis based on a linear model that applies the method of variance decomposition to reduce the dimension of multidimensional data to extract the most important elements and structures in the data. PCA can extract two coordinate axes that reflect the differences between samples to the greatest extent to reflect the differences of multidimensional data on the two-dimensional coordinate map and then reveal the simple laws in the complex data background. NMDS is a nonlinear model whose purpose is to overcome the shortcomings of linear models and better reflect the nonlinear structure of ecological data. NMDS analysis is applied to reflect the species information contained in the sample in the form of points in the multidimensional space, while the difference between different points is reflected by the distance between points, which can reflect the differences between groups or within groups of samples. Based on the species abundance tables at different classification levels, we conducted PCA and NMDS analysis. If the species composition of the samples was more similar, then the distance between them was closer, as shown in the PCA and NMDS diagrams in Principal coordinate analysis (PCoA) extracts the most important elements and structures from multidimensional data through a series of eigenvalues and eigenvector ordering. We conducted PCoA based on the Bray–Curtis distance and selected the principal coordinate combination with the largest contribution rate for graphic display. If the sample distance was closer, the species composition structure was more similar. Therefore, the samples with high similarity in community structure tended to gather together, and the samples with large differences in community were far apart. The Bray–Curtis distance matrix was obtained based on species abundance tables at different taxonomic levels. The results of the PCoA analysis based on the gate level are shown in To study the similarity of different samples, a clustering tree of the samples was constructed by clustering the samples. The Bray–Curtis distance is the most common distance indicator used in systematic clustering. It is used mainly to describe the similarity between samples, and its size is the main basis for sample classification. Starting from the abundance table of genes in each sample, the Bray–Curtis distance matrix was used to perform cluster analysis among samples, and the clustering results were integrated with the relative abundance of species at the phylum level in each sample for display. The results are shown in To screen the species biomarkers with significant differences between groups, first, the rank sum test method was used to detect the different species between different groups, and linear discriminant analysis (LDA) was used to achieve dimensionality reduction and evaluate the influence of the different species; that is, the LDA score was obtained. The LEfSe analysis results of species with differences between them included three parts, namely, the LDA value distribution histogram, the evolutionary branch diagram (phylogenetic distribution), and the abundance comparison diagram of biomarkers with significant differences between groups. The LDA value distribution map and evolutionary branch map of different species are shown in Resistance genes are ubiquitous in both human intestinal microorganisms and other environmental microorganisms. The abuse of antimicrobials leads to irreversible changes in the microbial community in the human body and the environment, which poses risks to human health and the ecological environment. Therefore, research on resistance genes has received extensive attention from researchers. CARD is a resistance gene database that has emerged in recent years. It has the advantages of comprehensive information, user-friendliness, timely updates and maintenance. The core component of this database is antibiotic resistance ontology (ARO), which integrates information such as sequence, antibiotic resistance, mechanism of action and association between AROs and provides online interfaces between ARO and databases such as Protein Data Bank (PDB) and the National Center for Biotechnology Information (NCBI).Unigenes were compared with the CARD database using the resistance gene identifier (RGI) software provided by the CARD database (v2.0.1) .According to the comparison results of RGI and the abundance information of unigenes, the relative abundances of AROs were calculated.Based on the abundance of ARO, the following were generated: the column diagram of abundance, the heatmap of abundance clustering, the circle diagram of abundance distribution, the analysis of ARO differences between groups, and the species attribution analysis of resistance genes (unigenes annotated to ARO) .Based on the relative abundance table of resistance genes, we calculated the content and percentage of ARO in each sample and screened the top 20 AROs with the largest abundance. The results are shown in To more intuitively observe the abundance ratio of ARO in each sample as a whole, and more intuitively display the overall distribution of ARO abundance, the ARO with the largest abundance of TOP10 was selected to be drawn as an overview circle, as shown in The resistance mechanism of resistance genes in the CARD database was classified, and the following resistance mechanism distribution map was drawn according to the relationship between the action mechanism of these resistance genes and species. The results are shown in The sequencing results clearly showed that the abundance and relative percentage of drug resistance genes in the eight typical samples showed a trend from high to low.tetW, tetW/N/W, lnuA, mdtB, lnuC, ANT6-la, ErmB, mdtC, ErmQ, tetBP, vatE, evgS, acrB, cpxA, mefA, Escherichia coli-ampc, tetL, yojl, AcrF and mdtA.The relative abundance and relative percentage of drug-resistant genes showed the following trend of descending order: All groups administered enrofloxacin and oregano oil did not develop a drug-resistant phenotype during the 5-day treatment period, as grouped in this trial.One of the most important functions of the gut microbiota is to prevent bacterial overgrowth and infection by pathogenic microorganisms, mainly through the production of antibacterial substances, site-occupying protection, and competition for nutrients with pathogenic bacteria.Escherichia coli. In addition, metabolites such as short-chain fatty acids (SCFAs) produced during bacterial metabolism can reduce intestinal pH, thereby inhibiting the growth and reproduction of exogenous bacteria. At the same time, the acidification of the intestinal environment promotes the acceleration of intestinal peristalsis, resulting in exogenous bacteria not interacting with the mucosa, and exogenous bacteria are excreted. The space-occupying protective effect of intestinal flora is to form a biofilm by closely combining with intestinal mucosal epithelial cells, thereby effectively preventing pathogenic bacteria from contacting the intestinal mucosa. Under the limited nutrient intake of the host, intestinal flora, especially symbiotic bacteria, have a natural competitive advantage for these nutrients, inhibiting the growth and reproduction of pathogenic bacteria.Bacteriocins are antimicrobial peptides or complex proteins synthesised by the ribosomes of various Gram-positive and Gram-negative bacteria, and have bacteriostatic and bactericidal effects. For example, the bacteriocin-like substances secreted by lactic acid bacteria have broad-spectrum antibacterial effects and can inhibit the growth and reproduction of Gram-negative bacteria such as The diversity and distribution of drug resistance groups in pig and chicken faeces under different feeding modes (free range and factory feeding) were systematically analysed.Based on the basic situation of the intestinal flora of animals (pig and chicken) in central China, the drug-resistant phenotype presents dual traces of history and current practice. Tetracycline resistance remains the most abundant type of resistance, and this result is consistent with the findings of other Chinese scholars in other provinces ,30.Some factory-farmed pigs and chickens have higher levels of intestinal bacterial resistance than free-range pigs and chickens.In this study, an integrated gene set of gut microbes was constructed using the metagenomic sequencing data of eight groups of swine and chicken gut microbes from different feeding patterns, combined with the gene sets of existing metagenomic sequencing data, which included 237,785 complete genes, of which 5% were unknown, expanding the comprehensiveness and completeness of the swine and chicken gut microbial gene sets. The genus-level dominant species represented by the 35 metagenomic assembled genomes were analysed by software. Using analytical software, we further revealed the diversity of swine and chicken gut microbiota composition and drug resistance phenotypes and identified differences in drug resistance phenotypes.Comparative studies in animal samples provide important links to understanding the global distribution of ARGs and the spread of multidrug-resistant bacteria, resistance exchange networks, and how different habitats and phylogenetic relationships influence the evolutionary dynamics of global antimicrobial resistance.Drug resistance in pathogenic bacteria is a growing threat to global health, and the development and spread of drug resistance in microorganisms is largely attributable to the misuse of antimicrobials.AMR in animals is country-, or even, region-specific. A map of the global distribution of resistance shows resistance hotspots in northeastern China, northeastern India and northern Pakistan, while resistance is just beginning to emerge in central India and central and southern China, Kenya, Uruguay and southern Brazil ,32,33,34There are differences in the intestinal microbiota under different feeding styles . AnimalstetW was the highest relative abundance resistance gene, followed by tetW/N/W, then lnuA; and others from high to low were mdtB, lnuC, ANT6-la, ErmB, mdtC, ErmQ, tetBP, vatE, evgS, acrB, cpxA, mefA, Escherichia coli-ampC, tetL, yojl, AcrF and mdtA.Metagenomic sequencing was conducted to study the relative changes of intestinal microflora and drug resistance genes in pigs and chickens in central China, after treatment amounts of enrofloxacin and oregano oil were added into the feed in factory and free range mode, respectively. The consistency of the trend of intestinal microflora changes and the existence of drug resistance genes were found after treatment, providing reference for rational drug use in the veterinary clinic. The results showed that


Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC. Lung cancer is a common and aggressive type of cancer in both men and women. The majority of the affected population is aged 70 or over, while a small proportion of subjects (1% or lower) diagnosed with lung cancer is younger than 45 . The treRadiomics is a high-throughput image analysis technique that derives a huge amount of quantitative imaging features. These features reflect the heterogeneity, texture, shape, and size of the tumor. The use of “radiomics” has efficiently supported cancer diagnosis, since imaging features reflect the tumor phenotype and staging . FurtherSeveral important studies on early diagnosis markers derived from modalities associated with either the phenotype or the genotype of a disease have been conducted . The fieIn radiomics studies, imaging characteristics are accumulated in a massive set of features, which entail either qualitative and quantitative image descriptors measured within the segmented volume of interest  or featuIn this work, a methodological framework for the investigation of the associations between the subcellular biomarkers measured through gene expression profiling and the radiomic features extracted from images is proposed. Through these associations, the radiomic features are biologically justified based on the enrichment analysis of their transcriptomics regression models. To this end, a rigorous marker selection procedure was performed in order to derive reliable transcriptomic markers that form the transcriptomics signature, with discrimination and predictive power, as well as stable radiomics markers that form the meta-radiomics signature, with representative distribution across the population. On the basis of these “single-modality” signatures, a combined analysis was conducted by performing regression analysis of radiomics based on transcriptomics, to generate reliable mappings for cancer associations between the two modalities, forming the p-metaomics features.The novelty of the current study lies in the rigorous methodological approach for the investigation of the transcriptomics features that are subsequently used for the modeling and biological interpretation of the radiomic features. Hence, the arbitrary radiomic features that describe the phenotype are modeled by significant genes, so that their use can be biologically justified by the transcriptomic substrate of that model.The overall design and methodology used is depicted in Most of the analysis was performed using MATLAB  with the exception of specific cases, stated in the text, where R (version 4.1.2) and Python (version 3.8.2) programming languages were used.Seven different datasets were used during the procedure analysis. The gene expression microarray data of all datasets were obtained from the publicly available Gene Expression Omnibus (GEO) database. In each dataset, the probesets were coded into their corresponding Entrez Gene ID according to the Illumina platform and, in cases of multiple mappings, the probeset with the largest gene expression value was used to express the corresponding Entrez Gene ID. The main dataset is dataset GSE28827 ,17,22,23Using the opensource Pyradiomics (version 3.0.1) Python package and the 3-D Region Of Interest (ROI) of the tumor of the scans, 749 radiomics features were extracted from 24 patients to quantitatively characterize the tumor region. Scans for 2 patients were excluded because the 3D ROI was smaller than 10 pixels. In order to efficiently process the images, several filters were used: the Laplacian of Gaussian, Wavelet, Square, Square Root, Logarithm, Exponential, and Gradient, and radiomics features were calculated for each filtered and unfiltered image. The features represent quantitative data and were classified in seven feature classes in compliance with the definitions introduced by the Imaging Biomarker Standardization Initiative (IBSI) ; they arSince our aim was to methodologically unravel the “radiotranscriptomics” interplay, we initially preferred to explore the above question based on the well-established microarrays transcriptomics data, which have provided researchers with a wealth of biological information and continue to enhance our understanding of disease etiology and pathogenesis, and progress in diagnosis and treatment. Six datasets that contain purely microarray transcriptomics data were used in our analysis. Dataset GSE75037 (abbreviated Dataset 2)  entails In our study, seven datasets  were useThe radiomics features were clustered in groups based on the similar patterns of the CT imaging features to produce compact groups, reducing the dimensionality of these features. The main dataset, Dataset 1, which was the only dataset that contained radiomic features, was used. From the overall analysis, 42 imaging features were excluded due to their constant value across all samples, with the values of the 707 remaining radiomic features scaled through standardization to homogenize their range of values.The iterative K-means algorithm  using thThe clustering was used as a dimensionality reduction technique, as the number of radiomics features used in the subsequent analysis was reduced into the number of derived meta-radiomics features that represent homogeneous clusters. However, the clustering procedure grouped together similar features based on their distribution over the entire population, rather than on their nature with the formation of the medical image. For instance, several characteristics of shape might be grouped together with textural characteristics, demonstrating compactness over their population performance rather than on their conceptual nature. Thus, the concrete meta-radiomics features signify characteristic distributions of radiomics features over the population.The gene expression values of cancer samples of Dataset 2 and the gene values of normal samples of Dataset 3 were used to identify genes of significant differentiation ability in a set of microarray experiments. The intersection of Dataset 2 and Dataset 3, which consisted of 16,252 common genes, was used for further analysis. However, due to the fact that the cancer and normal samples of each gene were derived from two different datasets, mean-centering normalization was applied as a preprocessing step in both datasets independently to restrict the “batch effects”   in R  by Tusher et al. , with thThe differentially expressed genes were further examined in terms of their statistical correlation with the radiomic features. Such correlations are considered important for cancer diagnosis in order to investigate the underlying biology and connection of the transcriptome and the phenotype. Dataset 1 was the only dataset that contained both microarray and imaging data for all the patients and it was thus used to investigate significant correlations between the CT imaging features and differentially expressed genes. The 42 radiomic features that had constant values across all samples were excluded from the analysis and thus the remaining 707 features were used for the correlation analysis. The significant correlations between the 707 radiomic features and the DEGs were investigated using two statistical methods: the Spearman rank correlation test and the SAM for quantitative problems.The non-parametric Spearman rank correlation test was performed between each pair of genes and imaging features, as in similar studies ,40. FurtIn order to assess and validate the predictive ability of the selected genes, the Support Vector Machine (SVM) classifier was performed using multiple kernels  to assess its performance; the linear kernel had the best performance. The SVM linear classifier was trained on the samples of Dataset 2 and tested on the external validation Dataset 4. Another validation test for the selected transcriptomic signature was performed by assessing the compactness of genes in each condition of the new Dataset 4. More specifically, the Biological Homogeneity Index (BHI) and the Biological Stability Index (BSI)  were calThe transcriptomics signature was extracted and validated for its diagnostic potential using DNA microarray data. Hence, two new DNA microarray datasets (Datasets 5 and 6) and one RNA-Seq dataset (Dataset 7) were utilized in order to evaluate the ability of the transcriptomics signature to discriminate between malignant and non-malignant tissue also on an RNA-Seq dataset. More specifically, the two external datasets with DNA microarray data (Datasets 5 and 6) were used to train an SVM linear classifier and the dataset with RNA-Seq data (Dataset 7) was used as the test set. The aim of this classification schema was to investigate whether the selected genes, which had been derived using their microarray expression profiles, can predict the type of lung tissue using their expression profiles extracted by the RNA-Seq technology, using three unseen datasets.The DEGs of the transcriptomic signature were extracted using datasets of normal samples and lung adenocarcinoma (LUAD), which is the most common subtype of NSCLC. The main goal was to identify genes that have differentiation ability between malignant and non-malignant samples. In the validation stage, the malignant samples were NSCLC cases derived from patients diagnosed with LUAD, but also with lung squamous cell carcinoma (LUSC), which are the two major subtypes of NSCLC. Thus, datasets that contained both LUAD and LUSC patients were utilized to validate the ability of the selected genes to differentiate between malignant and non-malignant samples, independent of the NSCLC subtype. Overall, the selected genes of the transcriptomic signature were well-validated for their differentiation ability between malignant (NSCLC) and non-malignant samples, even under the variability within the NSCLC samples stemming from the two different subtypes of NSCLC.The modeling of the radiomic features from the transcriptomics data was investigated using regression analysis. More precisely, we investigated the degree in which the power of radiomic markers can be modeled and approximated by molecular markers, giving a means to replace the actual radiomic measurements with approximate (or simulated) values obtained from regression models built on the basis of genes. Thus, this modeling methodology provides a tool for the formation of artificial imaging features from precursor molecular entities.The derived gene expression data  and the radiomic features data  from the main Dataset 1 were used in a multiple regression analysis with Lasso regularization , in ordeNotice that p-metaomics correspond to original radiomic features that correlate with the transcriptomic signature, and each p-metaomics feature is predicted from its corresponding model built from a subset of genes with non-zero regression coefficients rather than the entire transcriptomics signature. Additional validity metrics, including the Normalized Root Mean Squared Error , Pearson correlation between the predicted meta-radiomic feature and the actual one, and the Cross Validated Normalized RMSE, were used to assess the performance of the predictive models .The discriminative value of the modeled (or simulated) p-metaomics features, which span a combined space from the gene expression profiles, was explored by utilizing the radiotranscriptomics correlations in approximating the radiomics feature space from the transcriptomic vector space. Dataset 2 contains genes expression profiles from malignant and adjacent non-malignant samples, but it does not provide information on any form of imaging features. Thus, artificial model approximations of p-metaomics markers were produced using the corresponding regression coefficient for each significant gene in the model. To this end, a heatmap  with the values of the cancer and non-malignant samples of the derived p-metaomics signature was implemented to examine the discrimination ability of these simulated features in cancer versus control populations. Furthermore, the BHI and BSI metrics were calculated in R in order to assess the homogeneity of the class separation produced by the p-metaomics models. Thus, the samples of Dataset 2 were clustered based on the values of the p-metaomics features, to assess whether they can produce biologically homogeneous clusters based on the cancer and normal conditions of samples.A two-step process was performed to infer biological consequences from the derived gene sets. The first step revealed the enriched terms for each gene set via the WEB-based GEne SeT AnaLysis Toolkit  , whereasp-values and low adjusted-FDR values.WebGestalt is a web tool for functional enrichment analysis, used here to extract shared characteristics of derived gene sets at various stages of our radiotranscriptomics analysis. The current version of WebGestalt (2019) was used, and it supports a plethora of functional categories from well-known public databases, such as the: (i) knowledge databases, including the Gene Ontology (GO) database and the Molecular Signatures Database (MSigDB), that contains information for potential targets of regulation by transcription factors (TFs) or microRNAs (miRNAs); (ii) gene pathway databases, including KEGG, Panther, and Wikipathway; (iii) interaction network database BioGRID; and (iv) RNA-Seq database Firehose-Broad GDAC, which contains NSCLC datasets (lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) from The Cancer Genome Atlas (TCGA)) . Using ot-test or ANOVA, Kaplan–Meier estimator, and Cox model) on the lung adenocarcinoma datasets of CANCERTOOL (lung squamous cell carcinoma data were currently not available), we aimed to compare the relative expression of the derived DEGs of the transcriptomic signature against: (a) tumor versus healthy tissue, (b) different pathologic feature , (c) molecular characteristics of the tumors , and (d) disease progression  [CANCERTOOL is a web-based interface that provides access to transcriptomics cancer data selected for rich clinical annotation. Using statistical analysis tools  .The K-means algorithm produced 95 clusters of co-expressed radiomic features based on the scores in the Silhouette and Davies–Bouldin criteria. More precisely, this K achieved a Silhouette score equal to 0.41 and a Davies–Bouldin index equal to 1.01. Furthermore, the homogeneity score of each of the 95 derived clusters was computed to assess homogeneous radiomic clusters. Thus, 77 of the 95 clusters satisfied the criterion of a homogeneity score greater than 0.75, forming the meta-radiomics signature. Each of the 77 homogeneous clusters  consisteThe extraction of the transcriptomics signature aims to identify the most discriminant and representative genes in lung cancer to be identified as transcriptomic markers. SAM with 2-fold change identified 7014 significant genes with a q-value equal to 0%, using the gene expression profiles of the cancer samples of Dataset 2 and the normal samples of Dataset 3 (cancer versus control samples). Strengthening the discrimination power and controlling the batch effects by testing samples of the same dataset, 2415 of the 7014 genes remained significant according to the 2-fold change within Dataset 2 (cancer versus control). Projecting to Dataset 1, 2370 significant genes were identified, with 1540 being positive  and 830 being negative  significant. Taking the common transcriptomic features that were found to be statistically correlated with radiomic features in both statistical tests (SAM and Spearman rank correlation test with FDR), a set of 78 significant biomarkers for NSCLC that consists of differentially expressed genes, also preserving significant associations with radiomic features, was identified . The 78 In The transcriptomics signature was validated with the assessment of the predictive ability of the genes in tissue classification , using the external Dataset 4 as the test set. Since, the new Dataset 4 did not contain 5 of the 78 significant genes, these 5 genes were excluded from the analysis. The classifier showed great performance, achieving accuracy of 92.05%, sensitivity of 84.09%, and specificity of almost 100%. The high performance of the classifier, assessing all the validity metrics, indicated that these 73 genes have the added potential to predict or classify cancer or normal samples. Furthermore, the compactness of the genes was assessed using the external Dataset 4. The BHI and BSI of the first test, after performing clustering of samples based on the gene values, were equal to 0.856 and 0.833, respectively, indicating that a large proportion of samples with the same biological label were consistently grouped together into the same statistical class. In The modeling of the p-metaomics markers is based on the linear regression of meta-radiomics markers from the transcriptomic ones. Thus, 53 of the 77 radiomic models satisfy the criterion of R-squared greater than 0.70, revealing that 53 meta-radiomic features can be predicted from genes with an accuracy of 70% or more. The p-metaomics signature consisted of these 53 p-metaomics markers, which engage 449 original radiomic features. The subset of genes that predicts each p-metaomics marker differed over the regression models, but all 73 genes participated in at least one predictive model.p-values of the Pearson correlation coefficients being significantly less than 1%, indicating strong relationships between actual and predicted meta-radiomics features  and the Pearson correlation coefficient was greater than 0.88, with all the corresponding features . Howeverfeatures , which, In MUC4, PTGER3, PLEKHA6, CDHR2, PKIG, ZNF423, ZFPM2, and VEPH1. Hence, each of these 8 genes was multiplied by the corresponding coefficient, which had been calculated from the training of the regression model. Subsequently, these values were summated to derive the values of the p-metaomics feature p78 that simulates the “wavelet_1_original_glcm_SumEntropy” feature. This radiomic feature was biologically justified via the enrichment analysis performed on the set of these 8 genes.The p-metaomics features are simulated radiomic features derived by a linear combination of genes. For instance, the p-metaomics feature p78 represents the predicted values of the radiomic feature “wavelet_1_original_glcm_SumEntropy” that was derived with 78.1% accuracy  based ony-axis reveals the four dominant groups of p-metaomics features that are over- and under-expressed. The two groups of p-metaomics features in each category  indicate the different degree of their differentiation ability. For instance, the green group of under-expressed p-metaomic features can more accurately discriminate between malignant and non-malignant samples than the corresponding orange group, as shown in Similar to the heatmap in The top 10 enriched GO biological processes terms include nucleoside triphosphate metabolic process, digestion, acute inflammatory response, carbohydrate catabolic process, and response to radiation, as shown in PARP1, FANCD2, RAD9A, and NABP2, which operate via the ataxia-telangiectasia mutated (ATM) gene. Although DSBs occur normally during DNA replication, meiosis, and immune system development, they are the most hazardous lesions arising in the genome of eukaryotic organisms, and their efficient repair is crucial in maintaining genomic integrity, cellular viability, and the prevention of tumorigenesis. The ATM gene is critical in maintaining genomic integrity and plays a key role in the cellular DNA damage response. In response to DNA double-strand breaks, ATM phosphorylates downstream proteins involved in cell-cycle checkpoint arrest, DNA repair, and apoptosis [According to the enrichment analysis of the 73 DEGs in NSCLC, the DNA IR-Double Strand Breaks (DSBs) and cellular response via ATM was the most important finding. In our study, the genes involved in this pathway were poptosis .In order to establish a causal relationship between radiomics (phenotype) and gene expression (intermediate between genotype and phenotype), the regression models of radiomics based on transcriptomics were exploited to characterize the GO biological processes, pathways, network modules, and TF- and miRNA-targets, using the online tool WebGestalt. The most significant associations of radiomics markers with functional biological terms, as revealed by the gene sets in the p-metaomics models, are summarized in Since the gene sets of regression models stem from the same 73 gene signature, we would expect to find similar enriched terms as above, with some degree of variation in their ranking. Indeed, we found several same enriched terms that remained significant  or reached a slightly lower significance level , but mainly a higher significance level in one or more p-metaomics models  . FurtherFocusing on the 36 most significant p-metaomics models (FDR ≤ 0.1)  mRNA co-expression network modules were associated with enriched biological processes, such as the digestive system process, glycosphingolipid metabolic process, xenobiotic metabolic process, transcription initiation from RNA polymerase II promoter, and bicarbonate transport . The ineXenobiotic metabolism is of importance in tumors since biotransformation via the drug-metabolizing enzymes can result in either their activation or detoxification [fication . The lunfication . Transcrfication ,62. Glycfication ,64. Digefication . This frfication . Mitochofication ,67.FANCD2 and NABP2 (hSSB1). FANCD2 is implicated in the Fanconi anemia pathway, to orchestrate the maintenance of genome integrity and prevention from diseases including cancer. Currently different forms of FANCD2 are reported to have an oncogenic or tumor suppressive role [It is worth mentioning that the DNA IR-DSBs and cellular response via ATM, one of the most important findings according to 73 DEGs, demonstrated a strong statistical association with the highly coherent group of the p58-metaomics model. This association is induced by two genes involved in this pathway, namely, ive role . NABP2 pive role ,70.In addition, the TFs’ target analysis by WebGestalt revealed four enriched predicted TFs, namely, HNF1A, ALX1, AR, and GR, and their target genes in seven p-metaomics gene sets . HNF1A, SPINK1  that was conducted via CANCERTOOL revealed the high abundance of overlapping genes, and is presented as d et al. . Furtherectively . Finallyectively .These findings not only emphasize the importance of the derived DEGs, but also support and justify their integration into p-metaomics models of lung cancer. The mixture of biological processes by the combination of transcriptomic markers inflicted by p-metaomics models reveal specific thematic patterns that may form hidden interconnections worthy of further experimental evaluation in diagnosis, prognosis, treatment, and survival analysis, via the examination of cost-effective and non-invasive imaging protocols. To this end, the value of such a joint exploration should be further evaluated in larger independent radiotranscriptomics datasets.This study provides a methodological approach to investigate radiotranscriptomics associations based on the concrete assumption that the phenotype of cancer is strongly dictated by its associated genetic origin. It has been widely considered that transcriptomic markers can achieve a certain degree of cancer predictability ,80. CompThe joint signature with measured radiomics and transcriptomics markers can be interpreted as complementing information from the two biological scales. Benefiting from the modeling of the transcriptomic markers from readily available radiomics markers in clinical practice, several studies have tried to elucidate associations between radiomics features and gene signatures for effective diagnosis and prognosis ,82,83. IETV4 , STK32A, SPINK1, GOLT1A, KRT6A (adenocarcinomas—squamous cell carcinomas signature)) in the first case, and an overlap in one (KRT15) of the three most prominent genes in the second case. The differences in the derived gene signature are related to the different methodology followed by these studies and our study to answer different research questions and scope. In many cases, a comparison with our study was not possible. For example, a comparison with signature/gene set of Dataset 3 was not considered useful because of the inclusion of only normal lung samples from this dataset. Moreover, the reported significant genes in the independent datasets could not be compared with our study, since these studies follow different research directions. More specifically, Dataset 5 was utilized by Wei et al. [PRMT5 between the lung tissue paired  samples, Dataset 6 was used by Rousseaux et al. [The transcriptomic signature, which was used to build the regression models of radiomics, was thoroughly examined for its significance in NSCLC, validating its ability to also differentiate between the malignant and control samples in several external datasets. Hence, a set of highly discriminative genes in NSCLC was thoroughly selected and used to produce the radiotranscriptomic models. The transcriptomics signature consists of 73 genes which are compared with the gene signatures/gene sets obtained from the used Datasets 2 and 4. This comparison showed an overlap of five genes , and SPINK1 genes; (b) xenobiotic metabolism, bicarbonate transport, transcription, glycosylation, digestion, and developmental-related processes, as well as nuclear and mitochondrial DNA repair processes; and (c) TFs, such as HNF1, ALX1, AR, and GR, and (d) miR-143, miR-17-3P, miR-380-3P, and miR-29 family in NSCLC. Based on these observations, the p-metaomics models integrate quantitative radiomic features with thematic expression patterns, linking an important biological prospect to lung tumor characteristics. The presence of more than one thematic pattern in several p-metaomics models may reflect a multitude of alterations from a single biological or genomic etiology and forms a basis for further assumptions and experimental studies. Furthermore, the formation of p-metaomics models can motivate the design of novel imaging protocols that can specifically target radiotranscriptomic signatures [The enrichment analysis on the significant genes involved in each p-metaomic model yields relevant biological support to the corresponding radiomics features. Each model with its biological origin concerns not only the central feature (p-metaomics) but also a variety of radiomic features in the same cluster, expressing the same distribution over the studied population. Through the coupling of genes in the p-metaomic models, our analysis highlights the significance of (a) gnatures . The idegnatures . Smedleygnatures  identifiThe histopathological features in the form of semantic features specified by the clinician could be associated with and related to the transcriptomic substructure of the tumor in NSCLC. Although this was not a primary research direction for this study, based on the results presented by Yip et al. , many ofThis is a pilot study, which presents a methodological framework for the investigation of radiotranscriptomics correlations to reveal the underlying biological connection. However, this study has several limitations. A certain limitation is the small sample size of the simultaneously available microarray transcriptomics and imaging data, since only one dataset of a relatively small number of patients with both types of characteristic features was used. The absence of publicly available radiomics data combined with transcriptomics data restricts the capacity to investigate and further validate the significance of radiomic features as imaging-based biomarkers. The delivery of publicly available datasets with both radiomics and gene expression data for the same subjects is essential to facilitate research on radiotranscriptomics towards the development of robust models in cancer diagnosis and prognosis. Furthermore, the lack of imaging data from several clinical centers reduces the variability of the used data and does not reflect the heterogeneity of the patients with NSCLC among different centers. Additionally, there is not a standardization protocol for the extraction of the radiomic features, restricting the robustness and the reproducibility of radiomic features and making it difficult to compare the results with other studies.Future research on the counterpart regression direction, investigating the modeling of the gene expression profiles from the easily and non-invasively captured radiomics measurements, will be performed. The tedious procedures of measuring mRNA through gene expression profiles could be avoided, when the radiomic features become robust biomarkers of the disease. Moreover, the analysis conducted via CANCERTOOL showed that a considerable number of DEGs play a role in overall survival, disease-free, or metastasis-free survival . Since tThis study proposes a methodological framework for the investigation of the associations between the radiomics and the transcriptomics in NSCLC to provide biological meaning of the radiomics features based on their modeling with a set of carefully selected transcriptomics markers. The genetic environment, as a cause of biological abnormalities related to the disease environment, would also dictate the phenotypic outcome reflected by the anatomic aberrations in radiological images. The proposed pipeline focuses on the identification of radiomic and transcriptomic markers, which are considered to be the most representative for lung cancer, resulting in a substantial reduction in the number of markers. The genes of the transcriptomics signature were thoroughly selected for their significant statistical correlation with radiomic features and their ability to discriminate malignant from non-malignant lung tissue, assisting in the diagnosis of the existence of lung cancer. The central radiomics features were simulated using regression models of these genes in order to enhance the relationship between the generative genomics data and the phenotypic outcome of the radiomics data. The discrimination ability of the simulated radiomics data  between malignant and non-malignant samples was also confirmed, showing their predictive power. Through the enrichment analysis of the genomic substrate of the p-metaomics features, a biological interpretation and support of the conceptual meaning of the radiomics were provided, revealing biological processes and pathways. The derived p-metaomics models provide a comprehensive approach for mapping the radiomic features with the biological information in NSCLC, and possibly in other cancer types, and can be expanded to answer multiple clinical problems related to cancer.