ITS, matK, rbcL, trnH-psbA), and sequencing. Sequencing data were assembled using Bioedit software, and the BLAST algorithm was used on the NCBI database for species identification at the genus level. Molecular barcoding data based on genetic similarity identification was in full coherence with taxonomic classification based on morphological data. The combination of ITS and matK exhibited a greater capacity to identify a species among the Greek salep samples. Out of the 53 samples examined, 52.9% were classified as Dactylorhiza spp. and 33.3% as Anacamptis spp., whereas only 6 samples were identified as Orchis spp. (11.8%). Given that a superior-quality salep beverage comes from tubers of the latter, the number of samples classified as such in northwestern Greece is unexpectedly low. A database of 53 original reference sequences from wild-growing samples of Greek origin was generated, providing a valuable resource for the identification of other salep samples from different regions. The DNA barcoding results unveiled that salep samples from northwestern Greece are related to nine members of four different genera of Orchidaceae. All species are nationally protected and covered by the CITES convention, while many of these orchids are included in the EU Directive 92/43/EEC appendix as “Other Important Species”. Thus, expedited coordinated management actions are needed to ensure their survival in the future. Molecular DNA barcoding combined with botanical taxonomy can be used for the identification and conservation of collected Greek orchids used for salep production as well as in the regulation of fair salep trade. A modified CTAB protocol was used for DNA extraction, amplification of barcoding regions ( Consequay 2023) ,6,7. Gerard’s Herbal in 1633 [The commercial product commonly called ‘salep’  is the most famous folk preparation comprising orchids . For tho in 1633 . To date in 1633 ,20,21. I in 1633 ; however in 1633 ,10,15,19 in 1633 . Anacamptis coriophora (L.) R.M. Bateman, Pridgeon & M.W. Chase, Anacamptis morio (L.) R.M. Bateman, Pridgeon & M.W. Chase, Anacamptis papilionacea (L.) R.M. Bateman, Pridgeon & M.W. Chase, Orchis anthropophora (L.) All., and Orchis italica Poir. [Anacamptis pyramidalis (L.) Rich., Dactylorhiza sambucina (L.) Soó, Dactylorhiza saccifera (Brongn.) Soó, Orchis militaris L., Orchis provincialis Balb. ex Lam. & DC., and Orchis simia Lam. [Orchis mascula [Orchis mascula salep has presumed lung anticancer activity, strong antioxidant activity, and beneficial effects on hyperlipidemia and hypertension [Currently, the most popular form of salep is a hot beverage that is consumed to soothe the throat and ease stomach aches and intestinal cramps and as a remedy for colds and coughing . Severalca Poir. , or Anacmia Lam. . One waymia Lam. . Among d mascula . Apart frtension . Undoubtrtension .Orchis, Anacamptis, and Ophrys and also Dactylorhiza, Himantoglossum, Neotinea, Platanthera, and Serapias are reported as common among these studies [Given the ethnic importance of salep and the concomitant concerns for orchid conservation ,10,15,19 studies .ITS), and chloroplast DNA, such as rbcL, maturase K (matK), psbA-trnH, and the transfer ribonucleic acid leucine (trnL), are broadly used as markers for the molecular identification of plant species [psaB, trnL-F, and nuclear Xdh [ITS and matK markers can successfully identify members of the large genus Dendrobium in the family Orchidaceae [nrITS, trnL-F, and matK for barcoding DNA extracted from 150 collected tubers of Iranian orchids used to produce salep [Orchis, Anacamptis, and Dactylorhiza. Specifically, nrITS and the trnL-F spacer proved to be easier to amplify and sequence than the matK, providing a better-discriminating ability that eventually led to the recognition of the species that are most threatened by overharvesting in different regions of Iran [The above-mentioned studies coupled with the complex taxonomy of Orchidaceae ,26,27,28 species . Howeverlear Xdh ,26,27. Ilear Xdh ,33,34. Pce salep , reveali of Iran . Thus thGenomics technologies and approaches are of vital importance to maintain and conserve biodiversity as they can provide reliable results, even if DNA is too degraded and difficult to sequence using next-generation tools . HoweverThe scope of this study was first to check whether widely used DNA barcoding markers already used in other Orchidaceae studies can be used for the identification of collected wild-growing orchids used for salep by the Sarakatsani ethnic Greek population subgroup in northwestern Greece . The secITS and matK were successfully amplified, sequenced, and used for the identification of almost all samples, while three barcoding regions  were successfully used for only four samples. Although in most samples the trnH-psbA region was amplified and sequenced, the results were in some cases divergent from the results of the other two barcoding regions that were totally in agreement. Divergent results are accompanied by low BLAST similarities with our samples. A lack of information or taxonomic misclassifications in public DNA databases could lead to divergent results and misidentifications. In only a few samples, trnH-psbA was used instead of one of the other two regions for molecular identification. No dependence of the sequencing success rate on the origin of the sample (fresh or dried tissue) was observed. The combination of ITS and matK demonstrated a greater capacity to identify a species among the Greek salep samples, in agreement with previous studies concerning the family Orchidaceae [Orchis sp. due to out-flowering appearance  or O. quadripunctata Cirillo ex Ten. . In some other cases, the molecular barcoding data suggested the identification as Dactylorhiza maculata (L.) Soó or Dactylorhiza sambucina (L.) Soó for the specimen GR-1-BBGK-18,6097-26, and D. maculata for the specimens GR-1-BBGK-19,6097-27 and GR-1-BBGK-16,6097-29 , and the specimen GR-1-BBGK-18,6097-3 as Anacamptis sp.  Rich., all other samples (n = 16) were found to be Anacamptis morio (L.) R.M. Bateman, Pridgeon & M.W. Chase with molecular barcoding data; the latter belong to subsp. caucasica (K.Koch) H. Kretzschmar, Eccarius & H. Dietr. as determined using the taxonomic identification of the collected samples  P.F. Hunt & Summerh. subsp. pythagorae (Gölz & H.R. Reinhard) H.A. Pedersen, P.J. Cribb & Rolf Kühn  Kreutz; sample GR-1-BBGK-21,146) and Dactylorhiza sambucina  were only identified as Dactylorhiza sp. with molecular barcoding data due to contradictive matching results in the NCBI database. The taxonomically identified sample GR-1-BBGK-18,6097-24 as D. sambucina was matched as either D. sambucina or D. incarnata (L.) Soó with molecular barcoding data; however, it is highly possible that this sample may be D. sambucina due to ITS barcoding accuracy (Dactylorhiza maculata (L.) Soó subsp. saccifera (Brongn.) Diklic (synonym of D. saccifera (Brongn.) Soó subsp. Saccifera) was only identified at the species level with molecular barcoding data (n = 6 samples); a possible molecular match with D. maculata subsp. fuchsii (Druce) Soó for the specimen GR-1-BBGK-21,119  [D. sambucina or Dactylorhiza viridis (L.) R.M.Bateman, Pridgeon & M.W.Chase (synonym Coeloglossum viride (L.) Hartm.) was rejected  Schltr. subsp. rumelicum (H.Baumann & R.Lorenz) Niketic & Djordjevic  was only identified at the genus level with molecular barcoding data (H. caprinum (M.Bieb.) Spreng. (synonym H. affine (Boiss.) Schltr.); however, the latter is not reported as currently present in the Greek territory  [The taxonomically identified accuracy . The taxK-21,119  was disray 2023) . In anotrejected  due to ting data  due to pay 2023) . In geneDactylorhiza (52.9%). Seventeen samples were classified as Anacamptis (33.3%)   . In manys Orchis . Surpris (11.8%) .Orchis spp., the number of samples classified as such in the sample set from northwestern Greece is unexpectedly low. According to the BLAST results, the Greek samples probably belong to Orchis pallens or Orchis quadripunctata. A closer inspection of the ITS barcoding region nucleotide alignment analysis indicates that the Greek samples are highly similar to O. pallens, and thus are different from other Orchis spp.  [Along this line and with the aim to protect orchid resources, previously published recommendations have suggested the need for long-term monitoring of wild-growing salep orchid populations in Greece and elsewhere and effective modeling regarding the response of different salep species to different harvesting pressures . Other ray 2023)  for in say 2023) . With onay 2023) , and manPlant samples of Greek orchids used for salep were collected from mountainous areas in northwestern Greece (mainly) during 2018–2022 . Information regarding the localities and habitats where salep orchids naturally grow in the wild was obtained through informal conversations with (a) local residents of mountain areas in northwestern Greece who collect bulbs for personal use and (b) shepherds of the Sarakatsani ethnic Greek subpopulation group who traditionally graze their flocks at high altitudes and are aware of the local flora . These pAll collections were performed between May and early July each year. When not possible to collect living plant individuals, dried inflorescences were collected . The wilhttps://powo.science.kew.org, accessed on 1 May 2023) [https://portal.cybertaxonomy.org/flora-greece/intro, accessed on 1 May 2023) [To allow comparisons across geographical scales and with other studies, all the species and subspecies are hereby cited with their currently accepted names according to the POWO (Plants of the World Online) database (ay 2023)  and not ay 2023) . The latTotal genomic DNA was extracted using a modified CTAB protocol . The extInternal Transcribed Spacer (ITS) [trnH-psbA intergenic spacer region [maturase k (matK) [ribulose-bisphosphate carboxylase gene (rbcL) [Four barcoding regions were tested, namely, er (ITS) , trnH-psr region , maturask (matK)  and ribue (rbcL) . The useDiuris sulphurea R.Br., Goodyera schlechtendaliana Rchb. f., and Caladenia reticulata Fitzg. were used herein as outgroups based on previous references from the literature  and taxonomic classification of orchids collected from different sites in northern Greece showed that 53% represent members of the genus Dactylorhiza, 33.3% are members of the genus Anacamptis, and 11.8% are members of the genus Orchis. Considering the reasons that sometimes create boundaries in DNA barcoding, reliable projects require holistic approaches given that biological and technical complications/issues are inevitably insurmountable. The use of such straightforward DNA barcoding protocols for salep orchids may assist in tracking, monitoring, and regulating the trade of wild-harvested products  and can also facilitate the conservation of natural populations of Greek salep orchids. The investigation herein has shown that DNA barcoding markers widely used in different orchid-related studies can also be used for the identification of wild orchids collected for salep by the Sarakatsani ethnic Greek population subgroup in northwestern Greece. The combined molecular barcoding (Although in situ conservation of these orchid plants is ensured in various sites of the Natura 2000 network in Greece, there are still many wild-growing salep orchid populations outside the protected zones that suffer extensive over-collection directly from the wild. Therefore, long-term monitoring of wild-growing salep orchid populations is needed in Greece, which should be combined with ecological modeling regarding the response of different salep species to varied harvesting pressures. In addition, collection bans should be enforced in severely affected areas and in cases of illegal collections and exports from the country, while close monitoring of species diversity in extant salep trade should be coupled with informed controls at domestic customs offices. Training on sustainable collection practices and facilitation for the establishment of small-scale pilot cultivations of salep orchids are already in place in Greece, and these should be extended to foreign stakeholders with the aim to alleviate future collection pressure on wild-growing populations of these protected phytogenetic resources.


Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival . The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.The online version contains supplementary material available at 10.1186/s12014-023-09441-w. Prostate cancer (PCa) is the second most common malignancy with the fifth-highest mortality among the male population worldwide . The difSPOP, FOXA1, IDH1, and ETS fusion [et.al found that the proteomic features of prognostic biomarkers are superior to the genomic and transcriptomic features in 76 PCa patients [Genomic and transcriptomic studies have proposed PCa classifications, based on genomic alterations such as S fusion –11. HoweS fusion . Ankit epatients . This fipatients –16. Thuspatients . HoweverData-independent acquisition mass spectrometry (DIA-MS)-based proteomics analysis has been widely used for the exploration of novel biomarkers and therapeutic targets . PulseDIWe collected 667 formalin-fixed, paraffin-embedded (FFPE) prostate tissue samples from 487 Chinese patients, including 182 paired tumor and adjacent benign samples, 271 unpaired tumor samples, and 32 unpaired adjacent benign samples  between the tumor and benign groups  and the other is from TCGA. For the aforementioned 13 proteins, they were found in both datasets Fig. B. While Additionally, we compared the mutations and copy number alterations (CNAs) in the seven pathways of the two subtypes using genomic data from TCGA and MSKCC. The highest CNA burden was found in PPS2 in both datasets  was then used to construct the DIA-based library using a library-free strategy. Next, the algorithm parameters were set to “unrelated runs” and “match-between-runs (MBR)”. Mass accuracy, MS1 accuracy, and scan window were set to 0 to allow for the automatic optimization by DIA-NN. Trypsin was selected as the digestion enzyme, and missed cleavages were set to 1. Carbamidomethylation was set as a fixed modification, while N-term methylation excision and methionine oxidation were set as variable modifications. The false discovery rates (FDRs) for peptides and proteins were set to 1%. Other parameters were left to their default values, with the exception of “protein inference”, which was set to “protein names” (from FASTA). The background used was a human FASTA file downloaded from the UniProt proteome dataset on January 26th, 2020.The raw files were then re-searched using our in-silico DIA-based library. The parameters were set as in the above-described step. Next, the peptide files were combined as described in a published report . After fThe pathway enrichment of the differentially expressed proteins (DEPs) was performed using STRING   was used to determine the difference among different GS grades. P-values were adjusted by the Benjamini & Hochberg method.A two-sided unpaired Welch’s The average protein quantities in each GS grade were used for fuzzy c-means clustering with the R (version 4.0.2) package Mfuzz (version 2.48.0). The number of clusters was set to ten and the fuzzifier coefficient, M, was set to 1.25.The enrichment analysis of pathways was performed using the “enricher” function from the “clusterProfiler” package  (defaultWe performed K-means clustering (with the “kmeans” function in R), consensus clustering (the “consensusClusterPlus” package in R), and NbClust testing (the “NbClust” function in R) to determine the optimal number of stable PCa subtypes. We scaled each sample to cluster them based on the constituent pattern of each pathway. Then consensus clustering was used to assess the robustness of the K-means clustering . NbClust testing provided 30 different test methods for determining the optimal number of clusters. A silhouette analysis was then performed to confirm the robustness of the clustering.We first excluded samples without survival follow-up data. Then, we randomly divided the data into a training set (80% of the samples) and a test set (20% of the samples). Using the training data, we constructed a Cox model and applied it to predict the risk scores for the test data. Subsequently, based on the median of the risk scores in the test dataset, the samples were divided into high- and low-risk groups. Finally, Kaplan–Meier curves were generated for the high- and low-risk groups in the test dataset.Seven signaling pathways consisting of 13 genes were evaluated. For each PCa subtype, we computed the fraction of samples with at least one alteration in each of the seven signaling pathways and then compared the two subtypes. A tumor sample was considered pathway-altered if one or more genes from a specific pathway contained a recurrent or known driver alteration.Additional file 1: Table S1. Information of patients and samples. Table S2. The differentially expressed analysis of PCa. Table S3. The proteomic pathway-based classification for PCa.Additional file 2: Figure S1. Differentially expressed proteins. A Pathway enrichment of the dysregulated proteins from comparing tumor and adjacent benign samples  from STRING [B Mfuzz clustering analysis of protein expression across the different groups . Figure S2. Consensus clustering of the proteomic data. The subgroups are identified based on proteomic data by K-means consensus clustering upon their abundance. Figure S3. Genomic analysis for the proteomic pathway-based subtypes. A The genomic alterations in the seven pathways were compared among the three clusters of proteomic-pathway-based subtypes, including gene mutation frequency (green), amplification frequency (red), and deletion frequency (blue). B Sankey diagrams for the mutation frequencies of the genes showing significant P-value (P<0.05) in the comparison between all possible pairs between the two subtypes. The color of the gene name represents the subtype where each gene shows the highest mutation frequency. ANOVA P-value: * <0.05; ** <0.01; *** <0.001.m STRING  (P-value


Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application.n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy.We stratified 69 primary IDH-wt GBM patients into TMZ-resistant  and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher’s exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP).In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs.The online version contains supplementary material available at 10.1186/s13073-023-01165-8. Temozolll death . Despitell death .6-methylguanine-DNA-methyltransferase (MGMT), a protein that repairs the damages from TMZ, is the most widely used predictor of TMZ response in GBM [MGMT methylation alone was not sufficient. Besides, it was believed that the relapsed GBM is driven by invasive GBM stem-like cells (GSCs) [Identifying non-responders of TMZ in advance is especially important in neuro-oncology. To date, the promoter methylation status of Oe in GBM . Howevers (GSCs) . Therefos (GSCs) . While ps (GSCs) , 10, we To identify predictive features and establish an integrative method to distinguish TMZ responder and non-responder before TMZ chemotherapy, we cultured a panel of GSCs derived from newly diagnosed treatment-naïve IDH-wt GBM patients and analyzed genomic traits and drug screening data of their early passages. Our recent work showed that these patient-derived GSCs better represent the traits of parental tumors compared to conventional cell lines –13. In tMGMT promoter was assessed by methylation-specific polymerase chain reaction (PCR) after sodium bisulfite DNA modification, and the mutation of IDH1 was detected by peptide nucleic acid-mediated clamping PCR and immunohistochemistry on the tumor tissues [n=69) for downstream analysis. For intratumoral heterogeneity analysis, 18 patients with multi-sector samples (52/128) were included (multi-sector cohort). Longitudinal GBM cohort (n = 40 pairs) for longitudinal expression analysis included 4 (2 pairs) out of 128 samples in addition to 64 samples (32 pairs) from Wang et al. [After written informed consent was obtained, we utilized tumor specimens of patients whose first therapeutic intervention was an open surgical resection at the Samsung Medical Center in accordance with the Institutional Review Board. Overall, 128 GBM specimens  were collected from 92 GBM patients with median age at diagnosis 57 (range 29-80) including 39 females and 53 males. GBMs were diagnosed based on the World Health Organization (WHO) criteria. The methylation status of the  tissues –17. Follg et al. , 20. DetEnrolled tumor specimens were enzymatically dissociated into single cells and cultured as described previously . These c2 humidified incubator, cell viability was assessed using an adenosine triphosphate assay system based on firefly luciferase . Cell viability was measured using an EnVision Multilabel Reader (PerkinElmer). Control wells containing only cells and vehicle (dimethyl sulfoxide) were included on each assay plate. The half maximal growth rate (GR) inhibitory concentration (GR50) and traditional area under the curve of the dose-response curve (AUC) were calculated using an online GR calculator [50 and AUC values were used to compute the z-score in a total of 126 GSC samples from GBM tumor specimens  and/or GliomaSCAN  were perSAVI2  was usedWe used the ngCGH python package (version 0.4.4)  to generFor samples with RNA-seq data but without WES, we estimated the CNAs from RNA-seq by adopting the CNAPE method  with sev2 fold change > 2.5, adjusted P < 0.01) for identifying differentially expressed genes resulting in four TMZ-resistant markers  by the STAR (Spliced Transcripts Alignment to a Reference) pipeline . Read coWe performed single sample gene set enrichment analysis (ssGSEA) using the GBM subtype gene sets defined by Wang et al.  on the Rhttps://gdac.broadinstitute.org/). Mutation and CNA data were downloaded from cBioPortal. Clinical data was downloaded from the original publication by Ceccarelli et al. [Transcriptomic data of the TCGA cohort was downloaded directly from Broad GDAC Firehose , CNAs, and expression levels of selected genes, were incorporated to train a machine-learning model, based on samples in the main cohort (n = 69). To address the issue of missing values, we first performed data imputation: for binary features, missing values were replaced by 0.5; for continuous features, missing values were imputed by KNNImputer [K=5). The imputed data was used to train the XGBoost model (python xgboost v0.90), where 50 decision trees with a tree depth of no more than 3 were constructed under the learning rate of 0.74 and the subsampling ratio of 0.35 for each boosting iteration. The above hyperparameters were selected via optimization of (1) AUC score in 5-fold cross-validation; (2) capability of stratifying patients with different survival outcomes in the training set; and (3) biological significance of prioritized features. In the final model, we used 0.6 as the probability cutoff to segregate two risk groups. Furthermore, we added L2 regularization to the cost function to control overfitting and enhance the generalization ability of our model for unseen data. Lastly, the area under receiver operating characteristic curve (AUC) score was used to measure the model’s performance.The XGBoost classifier  was traiNImputer  (K=5). TT-test, Wilcoxon rank-sum test, Spearman’s rank correlation coefficient test, and Fisher’s exact test were used to conduct different statistical analyses. Survival analyses were performed using the Kaplan–Meier method and the Cox proportional hazards regression method. Patients who were alive at the last known follow-up were considered censored in these analyses. Hazard ratios (HR) and their 95% confidence intervals (CIs) were calculated. Statistical analyses were conducted using Python (v.3.8) and R (3.6.3) software.n = 69, main cohort) obtained from surgically resected IDH-wt primary GBM specimens. Since conventional metrics such as the effective concentration at 50% (IC50) or maximum inhibition % (Emax) highly depends on cell division rate obscuring accurate sensitivity prediction, we adopted GR inhibition metrics, which are independent of division number and therefore superior to conventional metrics for assessing the effects of drugs in fast dividing cells [50 values for each sample, and for those with infinite GR50 values, we measured conventional AUC values. By calculating Z-scores for GR50 and AUC, we divided our samples into TMZ-Sensitive and TMZ-Resistant groups  [To evaluate GBM’s response to TMZ, we performed in vitro TMZ cytotoxicity assays in short-term (6 days) cultured patient-derived GSCs  .Fig. 1Inn = 29) and TMZ-Sensitive (n = 40) groups defined from in vitro sensitivity were highly predictive of survival outcomes for patients who were under a TMZ-based treatment regimen . Notably, the above-defined in vitro sensitivity surpasses the well-known MGMT promoter methylation status in predicting the patient prognosis  was marginally significant  and RNA-seq on 34 tissue specimens were either newly performed or downloaded from previous publications , 20 (Addn = 12) and sensitive (n = 22) samples. Principal component analysis on these 34 samples showed slight clustering by GBM subtype  but by no other factors including age, gender, and MGMT promoter methylation status  significantly up-regulated in the TMZ-resistant group . To explore the prognostic value of the TMZ-resistant marker genes, we extracted 96 RNA-seq available TMZ-treated IDH-wt primary GBM patients from the TCGA dataset and classified them into high-risk and low-risk groups based on the expression of these four genes. Notably, the high-risk group had significantly worse PFS (P = 1.59e−03 by log-rank test) and OS  tendency [NF2 mutation, hypermutation, 5-ALA positive and 5-ALA negative which were not available in the TCGA testing cohort  and OS  validating the power of our model to predict prognostic outcome in patients treated by TMZ. Moreover, we investigated the survival difference across four subtypes  in the TCGA cohort and no significant segmentation was observed for PFS (P = 0.531 by multivariate log-rank test) and OS (P = 0.412 by multivariate log-rank test) in Additional file P = 0.039 by Fisher exact test, Additional file P = 1.98e−02 by log-rank test, Fig. P = 1.26e−04 by log-rank test, Fig. MGMT promoter methylation status in the TCGA dataset (n = 203), our model integrating multiple features provided a better way to segregate patients with different outcomes of both PFS and OS  [Figure tendency  as anothers Fig. b. Notabl8/TMZEP) . Users cPTEN loss, EGFR gain, and deeper deletion of CDKN2A/B was observed specifically in the sensitive sectors of these two patients  is a key factor that causes therapeutic resistance and recurrence in GBM , 39. To P = 0.027; PFS, P = 0.015; by log-rank test), indicating that although the TMZ treatment achieved the particular effect by eliminating a sensitive group of tumor cells, the resistant GSCs might quickly lead to tumor relapse , followed by 43% (12/28) with three sectors, and 50% (3/6) with four sectors  pathway [MGMT expression [The expression level of the four TMZ-resistant markers predicted poor survival not only in our cohort but also in an independent IDH-wt GBM cohort extracted from the TCGA dataset. In addition, higher expression of these genes was observed in the recurrent GBMs and the TMZ-resistant sectors of the TMZ-ITH patients, highlighting the role of these genes in contributing to TMZ resistance. Although further studies will be needed to investigate the underlying mechanisms of these genes, it was reported that the  pathway . Compell pathway  and regupression .CDKN2A/B loss/deletion, PTEN loss/deletion, and EGFR gain/amplification more frequently in the TMZ-sensitive samples from the main cohort and the multi-sector cohort, while each single feature was not statistically significant. According to the fifth edition of the WHO classification of tumors of the central nervous system (CNS), EGFR amplification and +7/−10 copy number changes (PTEN in chromosome 10) are the parameters for Glioblastoma IDH-wt diagnosis, while CDKN2A/B homozygous deletion is the parameter to diagnose IDH-mutant astrocytoma as WHO CNS grade 4 [CDKN2A, PTEN, and EGFR CNAs affect TMZ-response in GBM is unknown and the investigation therefore belongs to the realm of future work.On the other hand, we observed co-occurrence of  grade 4 , suggest grade 4 –50. Hobb grade 4 . They sp grade 4 . Yet howConsidering that single features showed limited power to predict TMZ efficacy, we developed a machine learning model to integrate many features to predict TMZ responders. The model outperforms single feature models, which would assist the improvement of TMZ treatment on GBM patients. However, the model was validated only in the TCGA dataset, so the chance of over-fitting cannot be fully ruled out. Prediction results in other datasets may vary due to various reasons such as the clinical settings of the hospital and the treatment of the patients. Therefore, our model is yet preliminary to be directly applied to practice, and evaluation on a larger additional independent cohort would be necessary in future studies. In addition, we identified a new patient group with TMZ resistance within the MGMT methylated group, but due to the small sample size of these patients, additional follow-up is necessary to confirm these results.Although more accessible, our model’s prediction using multi-omic features is still less accurate than in vitro screening, partially due to that the current markers may not be complete, and more markers such as non-coding genomes or epigenomic features remain to be discovered. In addition, the features may not be independent, so more advanced multi-omics integration methods could be applied to reveal interactions between different data layers and further improve the model’s robustness. Moreover, utilizing single-cell sequencing or cell-type deconvolution technologies  to assess the TME composition as well as resistant and sensitive tumor samples could be promising future directions to further demonstrate how concordant the cell-type compositions can affect treatment outcomes.In summary, we demonstrated that in vitro TMZ screening of patient-derived GSCs can reflect treatment outcomes in IDH-wt GBM patients under the standard Stupp therapy (radiotherapy with concomitant TMZ followed by adjuvant TMZ). Genomic and transcriptomic characterization revealed MGMT promoter methylation status, hypermutation, and the expression of MGMT, EGR4, ANXA3, PARPA, and LRRC3, together with other features, as relevant molecular predictors of TMZ response for IDH-wt GBMs. The machine learning model TMZep  for predAdditional file 1: Table S1. Clinical annotations of 128 GBM samples used in the study. Table S2. Variants identified by WES and GliomaSCAN. Table S3. Copy number alteration by WES and/or RNA-seq. Table S4. EGFRvIII detection in RNA-seq available samples. Table S5. Input file used for machine learning training.Additional file 2: Fig. S1. Timeline of sample acquisition, sequencing, in vitro culture and TMZ screening. Fig. S2. Gaussian Mixture Model used to identify genes with the same expression profile between patient-derived cells (PDCs) and tumor tissues. Fig. S3. Principal Component Analysis and differentially expressed gene analysis on 34 tissue RNA-seq samples. Fig. S4. Association of MGMT promoter methylation status to survival and in vitro TMZ screening in the main cohort. Fig. S5. Copy number estimation by GliomaSCAN. Fig. S6. Copy number estimation by RNA-seq. Fig. S7. Machine learning model feature importance. Fig. S8. Correlations between GBM subtypes and TMZ response. Fig. S9. Comparison of survival prediction in TCGA cohort. Fig. S10. Genomic landscape of multi-sector samples. Fig. S11. TMZ-resistant marker expression and CNV comparison in patient M13 and M14. Fig. S12. Progression free survival difference in patients with multi-sector samples.Additional file 3. Supplementary methods.


Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns. This study comprehensively benchmarks 18 state-of-the-art methods for cellular deconvolution of spatial transcriptomics and provide decision-tree-style guidelines and recommendations for method selection. They are used to profile spatial locations of all detected mRNAs, providing a new perspective for biologists seeking to understand cells per se as well as their microenvironments. Broadly, spatial transcriptomics technologies can identify undiscovered transcriptional patterns and reconstruct transcriptional panoramas of whole tissues. On a fine-grained level, these technologies can be used to explore the interactions among neighboring cells and intracellular and extracellular states, which helps redefine the function of cells and improves our knowledge of diseases2. The current spatial transcriptomics technologies can be mainly classified into two categories. The first category is image-based technologies, including in situ sequencing- and in situ hybridization-based methods3, which can profile mRNA with high spatial resolution, especially at the subcellular level. However, limitations such as the low number of profiled genes, low sensitivity of mRNA detection, and time-consuming processes impede the broad application of image-based technologies. The second category is sequencing-based spatial transcriptomics technologies, which capture position-barcoded mRNA with nongene-specific probes. These technologies can profile the whole transcriptome of tissue sections of any size, and are more user-friendly and less time-consuming than image-based technologies4. Moreover, spatial transcriptomics technologies are highly applicable and have been used to improve our understanding of various species, organs, and tissues, including the brain5, liver6, and tumors7.Spatial transcriptomics technologies, named “Method of the Year 2020”One critical issue related to sequencing-based spatial transcriptomics technologies is low-resolution spots containing multiple cells with several blended cell types, which can conceal the genuine transcriptional pattern and lead to biological misunderstanding of the tissue resulting in the distorted cellular-level reconstruction of the tissue. An important task, therefore, is to quantify the proportion of all cell types among captured spots, so-called cellular deconvolution. Following deconvolution, all captured spots can be used to better understand intercellular functions and recover the fine-grained panorama of a heterogeneous tissue.8 was focused on single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics integration methods. There is another recent benchmarking study9, in which the number of related methods is limited and scRNA-seq reference-free methods are not considered. Despite these efforts, clear guidelines and solid recommendations for the users are still lacking for the comprehensive coverage of available methods.A recent benchmarking studyIn the present study, we conducted a comprehensive benchmarking and provided guidelines for the cellular deconvolution of spatial transcriptomics data. Specifically, we evaluated 18 existing computational methods with 50 simulated and real-world datasets by comprehensively testing the accuracy, robustness, and usability of the methods. These methods could be broadly classified as those with and without scRNA-seq references. Based on their computational techniques, we grouped the methods as follows: probabilistic-based, non-negative matrix factorization-based (NMF-based), graph-based, optimal-transport (OT)-based and deep learning-based methods. During benchmarking, we used multiple metrics and various data resources with different spatial transcriptomics techniques, spot resolutions, gene numbers, spot numbers, and cell types to ensure our assessment was comprehensive and to deepen our understanding of the cellular deconvolution methods. In addition to the quantification and visualization processes, decision-tree-style guidelines were produced, which included the refinement of the benchmarking results and the collection of respective additional features of the methods detailed in related publications. These guidelines recommend scenario-specific methods for users considering computational efficiency and the characteristics of data resources. The general limitations and future perspectives associated with cellular deconvolution are also discussed to give users a clear picture of the cellular deconvolution field and thus facilitate the improvement of tools for the community.10, Cell2location11, RCTD12, DestVI13, stereoscope14, SpatialDecon15, STRIDE16, NMFreg17, SpatialDWLS18, SPOTlight19, DSTG20, SD221, Tangram22, Berglund23, SpiceMix24, STdeconvolve25, SpaOTsc26 and novoSpaRc27. According to the data resources used, Berglund, SpiceMix, and STdeconvolve were scRNA-seq reference-free methods that identified cell-type-specific spatial patterns using only the information from the spatial locations of spots and their gene expression profiles without any reliance on external scRNA-seq data. The remaining 15 methods required scRNA-seq data from the same tissue as the spatial transcriptomics data. Cell-type annotations and cell-type-specific gene expression profiles from scRNA-seq data can help optimize the proportion of all cell types in the spatial transcriptomics data. The 18 methods were classified based their computational techniques as follows. Probabilistic-based methods: Berglund, Cell2location, DestVI, RCTD, SpatialDecon, stereoscope, STRIDE, and STdeconvolve; NMF-based methods: CARD, NMFreg, SpatialDWLS, SPOTlight, and SpiceMix; graph-based methods: DSTG and SD2; deep learning-based method: Tangram; and OT-based: SpaOTsc and novoSpaRc. These five computational techniques introduced different methods to formulate the spatial transcriptomics data (sometimes with the scRNA-seq data) and solve the cellular deconvolution problem 31, 10X Visium (Visium)32, Slide-seqV233, and stereo-seq35. Their corresponding scRNA-seq datasets were collected as complementary resources  theTo assess accuracy of methods, we used multiple metrics to quantify the performance of deconvolution, including the Jensen–Shannon divergence (JSD) score, root-mean-square error (RMSE), and Pearson correlation coefficient (PCC). For simulated data (MERFISH and seqFISH+), we used the JSD score and RMSE to measure the distance between the predicted cell-type proportion and ground truth (see “Methods”). Because seqFISH+ and MERFISH have single-cell resolutions, we binned them by size  to simulate low-resolution spots. According to the number of simulated spots and genes detected for seqFISH+ and MERFISH, seqFISH+  had a low number of spots but a high number of genes, whereas MERFISH (3067 spots and 135 genes) exhibited the opposite trend. These two data resources were complementary in terms of the number of spots and genes, and hence provided helpful results to observe the performance of all methods at these two extremes. For each sequencing-based dataset , we chose several cell types and their known marker genes to compare the PCC between the spatial distribution of cell-type proportion and marker-gene expression, which was used to quantify the performance of deconvolution without any ground truth  10,000, 6000, or 3000 genes were randomly chosen in the seqFISH+ dataset and 26,365, 18,000, and 9000 in zebrafish embryo dataset by stereo-seq; (2) three resolutions were simulated with 12 MERFISH datasets and zebrafish embryo dataset using binning sizes of 20, 50, and 100 μm and 5, 10, and 15 μm, respectively, which were also used to test performance with different numbers of spots; (3) 17 original cells types and 11 integrated cell types were tested in Slide-seqV2 datasets; (4) two kinds of normalization methods in the Visium dataset to test the effect of normalization of input spatial transcriptomics data; (5) three different values for each chosen hyperparameters in Visium and Slide-seq V2 datasets among five pairs of cell types and their marker genes; and (6) the stability of the performance was assessed by repeating the experiments three times with the seqFISH+ dataset using 10,000 genes per spot.32. We combined these two sub-cell types under the name “Cornu Ammonis” (CA), which was the former name of the hippocampus. The spatial patterns from the sub-cell types (CA1 and CA3) and integrated cell types were located from the results of 17 and 11 cell types in Slide-seqV2  performed better using raw data than using normalized data by lognorm  which could be used for quantitative evaluation as the ground-truth is known for such datasets, and real-world datasets generated by sequencing-based technologies  which could be used to mimic the real-world scenarios for cellular deconvolution tasks. Considering the performance and additional features of the methods, decision-tree-based scenario-specific recommendations and guidelines for method selection were proposed for users. We found that the performance of the 18 methods varied among multiple spatial transcriptomics technologies with different experimental conditions. Nevertheless, each method category contained at least one high-performing method. In general, CARD, Cell2location, Tangram, and RCTD were the best performing methods. Compared with the existing benchmarking studies12. Second, the high dropout rate of spatial transcriptomics is a traditional issue in scRNA-seq. Small libraries lead to deficient mRNA detection; thus, marker genes for rare cell types become undetectable16. The biological pipeline of spatial transcriptomics technologies should be improved further, even though this situation has been considered in SD2 and an imputation method has been proposed37.Following our assessment, we raise two general but crucial limitations that await solutions. First, the platform effect causes two problems as follows: (1) systematic variation in gene expression profiles between scRNA-seq and spatial transcriptomics data; owing to differences in technology-dependent library preparation and sequencing platforms, discrepancies in the detected mRNAs from the same tissue section are inevitable, especially in heterogeneous cancer tissue sections, and (2) variation between two modalities affects the mismatch of cell types between scRNA-seq and spatial transcriptomics data. The prior assumption of integrating these two modalities is to share the same cell types between scRNA-seq and spatial transcriptomics data. Even though RCTD solves the platform effect issue via a normalization strategy among all cell types, cell-type-specific platform effects warrant further exploration11. (3) Through the development of spatial transcriptomics technologies, the resolution of spots becomes higher, up to the subcellular resolution by recent technologies34. Although the rapid progress of spatial transcriptomics technologies is exciting, the marginal benefits of higher resolution are outweighed by the booming higher dropout issue which has more risks of losing some valuable information. In the future, under current sub-resolution technologies, the spatial transcriptomics technology for the single biological cells should be more essential to develop. (4) The cellular deconvolution of spatial transcriptomics will not only help biologists study the structure of tissues but also become associated with artificial intelligence–assisted computational pathology and the healthcare system.We also present possible future directions of the field to shed light on the development in the field. (1) Multimodal learning will likely become a hotspot in the development of cellular deconvolution methods used in spatial transcriptomics and its applications. For instance, bioinformaticians could use histological tissue images with image intensity levels that could improve our understanding of spatial transcriptomics. (2) Three-dimensional deconvolution and mapping of tissues will provide more novel biological insights than are currently provided by two-dimensional deconvolution. More spatial transcriptomics datasets with consecutive tissue slices are emerging, and the spatial context of interslices will provide more patterns that assist the deconvolution processRMSE was calculated between JSD was calculated as a score between Because ground truth does not exist in sequencing-based spatial transcriptomics data, we calculated the PCC between the predicted proportion of specific cell type We first assume that there are The methods used without a scRNA-seq reference, also called unsupervised methods, deconvolved low-resolution spots based on the gene expression profile and location of spots from spatial transcriptomics data only. STdeconvolve was inspired by the notion of discovering latent topics in collections of documents, which is a common task in natural language processing, and uses Latent Dirichlet Allocation to infer the proportions of cell types based on gene expression profiles in spatial transcriptomics. Berglund uses Poisson factor analysis and Monte-Carlo Markov Chain sampling to deconvolve spots. SpiceMix uses the locations of spots as an extra input in addition to gene expression profiles and incorporates graph representations of spatial relationships into matrix factorization to deconvolve spots.In general, unsupervised methods needed user to specify the number of topics which represented the clusters waiting for assigning the names of known cell types. Ideally, the number of topics should be similar to the number of cell types. In our evaluation, we set the number of topics as the true number of cell types manually among all datasets. After the deconvolution, a topic-by-spot matrix was generated, and we multiplied this matrix and inputted a spot-by-gene matrix to obtain the topic-by-gene matrix. We aimed to map the topics to real cell types for further evaluation. First, we summed up the same cell types in an annotated cell-by-gene matrix from the scRNA-seq data as the cell-type-by-gene matrix. For each real cell type from scRNA-seq data, we calculated the PCC between this cell type and all topics, chose the best-paired topic with the highest PCC, and assigned the name of current cell type to chosen topic. After assignment, this chosen topic would be ignored in the future steps. Then, we repeated the aforementioned steps on the next cell type until all cell types were iterated. For now, each topic should be paired with the best suitable cell type without duplication and topic-by-spot matrix could be easily transferred to cell-type-by-spot matrix for evaluating the performance of unsupervised methods further.Owing to the extremely high resolution (500 nm per spot) and dropout rate of stereo-seq data, it was necessary to integrate subcellular-resolution spots into low-resolution spots by binning them using a 100 × 100 spot square (bin100) with slides of 50 μm (100 × 500 nm) and summing their gene expression profiles. The bin100 stereo-seq data had a similar resolution as that of Visium, and it performed reasonably in deconvolution tasks. For the zebrafish embryo dataset by stereo-seq, it was binned by 5, 10 and 15 μm to test the robustness.12.To evaluate the robustness of the methods, we tested their performance in terms of different cell types. We integrated the 17 original cell types into 11 cell types, thereby combining some sub-cell types. We integrated CA1 and CA3 into “Cornu Ammonis”; Neuron.Slc17a6, Neurogenesis, and Cajal_Retzius into “Neuron”; Endothelial_Stalk and Endothelial_Tip into “Endothelial”; and Oligodendrocyte and Polydendrocyte into “Oligo_Poly”For marker gene selection in all datasets, most biological marker genes were chosen from publications. Specifically, we chose the top-five highly variable genes  for each specific cell type from Slide-seqV2 datasets as the marker genes.We constructed a summary table to show the performances of the methods Fig. . Because10: we used the code of CARD v1.0.0 from https://github.com/YingMa0107/CARD. We set minCountGene to 5 and minCountSpot to 5, which are the default parameter settings.CARD19: we used the code of SPOTlight v0.99.0 from https://github.com/MarcElosua/SPOTlight. We set cl_n to 10 and hvg to 2000.SPOTlight20: we used the code of DSTG from https://github.com/Su-informatics-lab/DSTG. We set learning_rate to 0.01 and epoch to 300.DSTG18: we used the code of SpatialDWLS from https://github.com/RubD/Giotto/, which integrates the SpatialDWLS method. We set min_genes in findMarkers_one_vs_all to 20 and gene_det_in_min_cells and min_det_genes_per_cell in filterGiotto to 5 and 5, respectively.SpatialDWLS2 21: we used the code of SD2 from https://github.com/leihouyeung/SD2, with the following settings: spot_num = 1000, lower_cellnum = 2, and upper_cellnum = 20.SD38: we used the code of NMFreg from https://github.com/tudaga/NMFreg_tutorial. The NMF function was used with the following parameters: number of components = 30, random_state = 17, and init = random.NMFreg14: we used the code of stereoscope v.03 from https://github.com/almaan/stereoscope. Analysis with stereoscope was conducted on a GPU with the following parameters: number of genes = 5000, st epochs = 75,000, st batch size = 1000, sc epochs = 75,000, sc batch size = 1000, and learning rate = 0.01.Stereoscope22: we used the code of Tangram v1.0.3 from https://github.com/broadinstitute/Tangram. The mapping of cells to space was conducted with the function tg.map_cell_to_space with mode = clusters.Tangram11: we used the code of Cell2location v0.1 from https://github.com/BayraktarLab/cell2location. The settings max_epochs = 4000, batch_size = None, and train_size = 1 were used.Cell2location25: we used the code of STdeconvolve 1.0.0 from https://github.com/JEFworks-Lab/STdeconvolve. We used the default settings, except that the number of factors was set correctly according to each dataset.STdeconvolve23: we used the code of Berglund 0.2.0 from https://github.com/SpatialTranscriptomicsResearch/std-poisson. We set the following parameters: –iter (=2000), --feature_alpha arg = 1, --mix_alpha arg (= 0.5), --phi_r_1 arg (= 1), --phi_r_2 arg (= 0.001), --phi_p_1 arg (=2), --phi_p_2 arg (=2), --theta_r_1 arg (=1), --theta_r_2 arg (=1), --theta_p_1 arg (= 0.050000), --theta_p_2 arg (= 0.950000), --spot_1 arg (=10), --spot_2 arg (= 10), --sigma arg (=1), --residual arg (= 100), --bline1 arg (=50), and --bline2 arg (=50) The MCMC inference options were set as follows: --MHiter arg (=100) and --MHtemp arg (= 1).Berglund24: we used the code of SpiceMix from https://github.com/ma-compbio/SpiceMIx. We selected the number of factors based on the used dataset and set use_spatial to True.SpiceMix12: we used the code of RCTD from https://github.com/dmcable/spacexr, which is integrated into a tool called spacexr (2.0.0). Spacexr (RCTD) was run with following the configuration: (1) create.RCTD was used with the parameter CELL_MIN_INSTANCE = 1; (2) run.RCTD was used in the doublet mode.RCTD15: we used the code of SpatialDecon from https://github.com/Nanostring-Biostats/SpatialDecon.git. SpatialDecon was run with the expected background count bg set to 0.01.SpatialDecon16: we used the code of STRIDE from https://github.com/DongqingSun96/STRIDE. The cell-type-associated topic profiles were obtained using the “STRIDE deconvolve” function. If not specified, STRIDE set the 75% quantile of nCount as the default scaling factor.STRIDE13: we used the code of DestVI (scvi-tools 0.16.0) from https://github.com/scverse/scvi-tools. DestVI first required genes with <10 counts to be filtered using the function “sc.pp.filter_genes.” To perform deconvolution, the single-cell model was then trained to learn the basis of gene expression with the scRNA-seq data for 300 epochs, whereas the spatial model was trained for 2500 epochs, with a learning rate of 10−3.DestVI26: we conducted the code of SpaOTsc from https://github.com/zcang/SpaOTsc.SpaOTsc27: we conducted the code of novoSpaRc 0.4.4 from https://github.com/rajewsky-lab/novosparc. Alpha was set as 0.5.novoSpaRcThe workstation used to test all methods had a 2 Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50 GHz  and 528 GB of memory. The GPUs were two Nvidia Quadro M6000 24 GB . The operating system used was Ubuntu 18.04.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Reporting Summary


Artificial intelligence (AI) approaches in cancer analysis typically utilize a ‘one-size-fits-all’ methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment. Unlocking the full potential of artificial intelligence (AI) in cancer analysis requires a departure from the conventional ‘one-size-fits-all’ approach. The concept of generalizing patient responses fails to acknowledge the complex and diverse conditions of individuals during diagnosis and treatment. This antiquated viewpoint inevitably leads to ineffective treatments, where success stories are overshadowed by disappointing outcomes. To truly revolutionize cancer analysis through AI, we must recognize and embrace the individuality of each patient.Gone are the days when we could rely on generalized patient responses as a basis for treatment decisions. This outdated approach often leads to suboptimal outcomes, where some patients experience remarkable success while others face disappointing results. The future lies in AI-enabled personalized medicine , offerinThis powerful tool is poised to significantly enhance the capabilities of clinicians, positioning them at the cutting edge of proactive and therapeutic interventions . By harnhttp://bib.oxfordjournals.org/), a multiomics dynamic learning framework that is meticulously designed to address the critical features and requirements essential for cancer analysis [Our study proposes a highly trustworthy multiomics learning (HTML) framework  to provide reliable predictions and enhance the explainability of the model .When all five modules of the proposed HTML architecture are integrated with each other, the resulting framework offers both high performance and significant explainability, making it an ideal multiomics integration model for clinical applications such as cancer diagnosis and survival analysis.HTML is an end-to-end model that can effectively improve the performance of multiomics classification tasks, enabling personalized diagnosis and treatment and facilitating the discovery of novel biomarkers. Our extensive research has led us to firmly believe that HTML constitutes the foremost approach to multiomics integration in biomedical data classification, characterized by high performance, trustworthiness and sensitivity to individual variations.We constructed a large-scale pancancer classification dataset of 33 different cancer types and 12 cancer subtype classification datasets from TCGA to showcase the effectiveness of HTML (Methods). Three types of omics data (DNA methylation (DNA meth), mRNA expression (mRNA), and micro-RNA expression (miRNA) except for the pancancer dataset) were used to enable multiomics integration and classification, leading to the elaboration of complementary information pertaining to cancers.http://bib.oxfordjournals.org/). In light of the potential impact of noise and redundant features on the overall quality, we conducted preprocessing and feature preselection on each type of omics data independently. http://bib.oxfordjournals.org/) display the precise feature quantity before and after preprocessing.In our experiments, only the samples that matched omics data were included in our study  and performed a comprehensive set of ablation studies  to demonstrate the crucial roles played by distinct components in HTML. The hyperparameter selection comparison for each dataset is shown in http://bib.oxfordjournals.org/. To provide a more valid estimate of HTML’s performance, we executed a 5-fold cross-validation and subsequently reported the mean and standard deviation (SD) of the results on the validation fold.We conducted a comparative analysis of the classification accuracy of HTML in relation to preexisting multiomics integration algorithms , macroaveraged F1 score (F1-macro), average area under the receiver operating characteristic curve (AUROC) and average area under the precision-recall curve (AUPRC) for each classification task. It is worth mentioning that we have incorporated an uncertainty metric in HTML to convey the model’s level of confidence in the classification results. In all of our experiments, we conducted the research on a single Tesla V100 GPU.To evaluate HTML’s performance in cancer-subtype classification, we compared the classification performance of HTML with the following ten existing classification algorithms :Naive Bayes (NB) .Linear regression (LR) .Support vector machine classifier (SVM) .Random forest classifier (RF) .Gradient tree boosting-based classifier (XGBoost) .Multi-Omics Graph cOnvolutional NETworks (MOGONET) .Multimodal Dynamics (MMDynamics) .Explainable Multiomics Graph Integration (EMOGI) .Subtype-DCC .Trusted Multi-View Classification (TMC) .http://bib.oxfordjournals.org/.All the above methods were trained with the same preprocessed data. All the evaluation metrics of the cancer subtype classification tasks are shown in http://bib.oxfordjournals.org/, HTML exhibits outstanding performance in the 33-class pancancer classification task, achieving an overall accuracy of 93.34 and an F1-weighted score of 92.22. Notably, we observed that the majority of misclassifications occurred within the same cancer meta-type, such as colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ), both of which are malignancies that manifest in the large intestine. Furthermore, a subset of ESCA samples is erroneously classified as HNSC, implying a potential correlation between these cancer subtypes.As shown in With regard to cancer subtype classification, it is evident that HTML outperformed other methods for most cancer subtype classification datasets. However, some exceptional cases emerged. For instance, the AUROC of MOGONET exceeded that of HTML on the GBMLGG dataset by 1.56%, but HTML still performed better than MOGONET regarding all other metrics. Moreover, although HTML exhibited incompetence with SVM and MOGONET in the STAD and KIPAN datasets, it surpassed all other models across the remaining metrics. Notably, the data imbalance in the UECE dataset, where the largest subtype contains ~15 times more samples than the smallest subtype, may inherently introduce instability into the AUROC and AUPRC results, which may explain the relatively weak results of HTML in the UECE dataset. Overall, HTML demonstrated a strong classification performance across the 12 cancer-subtype classification tasks while surpassing other state-of-the-art methods in all accuracy, F1-macro, and F1-weighted metrics.Regarding individual datasets, our HTML model achieved high cancer subtype classification results in some relatively straightforward binary classification tasks, such as ESCA, STES and KIPAN. However, the model faced challenges in tougher classification tasks such as GBMLGG and STAD. In the GBMLGG dataset, the model may have experienced difficulty distinguishing and classifying the three subtypes of astrocytoma (ASTRO), oligodendroglioma (ODG) and oligoastrocytoma (OA) due to their common origin from glial cells of the brain and spinal cord. Similarly, in the STAD dataset, there existed ambiguous relationships between the labels adenocarcinoma (ADC) and intestinal adenocarcinoma (IAC), wherein IAC identifies a distinct ADC subtype found in the mucous membrane of the intestines forming a subset of ADCs. This complex label relationship may have contributed to the model’s difficulties in accurately classifying these subtypes.HTML is an adaptable framework that can be proficiently utilized for cancer prognosis tasks. To elucidate this point, we chose four datasets, namely, GBMLGG, UCEC, COADREAD and SARC, to predict patient cancer development across diverse cancer subtypes. The Kaplan–Meier curves for risk prediction in these patients are presented in P value between low-risk and high-risk groups was <0.01. Similarly, in the UCEC dataset, patients with serous endometrial adenocarcinoma (SEAC) and mixed serous and endometrioid (MSEAC) were effectively stratified based on cancer progression risk. However, the prognostic performance for endometrioid endometrial adenocarcinoma (EEA) patients is not statistically significant, which is possibly due to a dearth of available samples. Moreover, the results from the COADREAD and SARC datasets further reinforce HTML’s prognostic capabilities. Taken together, these findings provide compelling evidence of HTML’s ability to accurately predict cancer progression in patients.Our analysis enabled us to effectively differentiate between high-risk and low-risk cohorts for each cancer subtype across all datasets. The low-risk group is represented by a solid line, while the high-risk group is denoted by a dotted line. For example, in the GBMLGG dataset, patients categorized as low-risk for oligodendroglioma (ODG), astrocytoma (AST) and oligoastrocytoma (OAC) demonstrated significantly superior cancer progression outcomes relative to their high-risk counterparts, and this difference was most pronounced in OAC subtypes, where the Intuitively, (i) the variation of impact for different features within a given sample on the model’s results should be distinct. (ii) The variation in attention given to a particular feature across different samples should be distinct. (iii) The variation in attention given to each feature during different training stages for a given sample should be distinct. Most existing encoder models learn a single set of weights for all inputs, which constrains their ability to fully explore the inherent characteristics of the features and provides understandable interpretations of the original data. To overcome this limitation, we introduced the feature dynamic concept, which adaptively learns feature dynamic weights based on the feature’s characteristics and distribution. In contrast to data preprocessing, which aims to convert raw data into an analysis-ready format by eliminating any anomalies or inconsistencies present in the dataset, feature engineering is devised to allot varying weights to different features across individual samples, enabling the model to focus intently on the most informative features of the dataset.Based on the analysis of the multiomics data, HTML can be used not only for the diagnostic and prognostic prediction of pancancer or cancer subtypes but also for self-adaptive assignment of cancer-related genes in specific samples through dynamic learning, thereby achieving dynamic monitoring of cancer progression and providing real-time guidance for individual precision treatment. In the randomly selected 66th sample , HTML aset al. [Similar to feature dynamic learning, the quality and noise level of data across modalities and samples vary. Therefore, there is a need for modulated prerequisites in the contribution of each modality toward the ultimate outcome along with the model’s confidence toward each modality that aligns with data quality. As a solution, we devised a modality dynamic learning module that imparts a confidence rating for each modality. As illustrated in et al.  reportedet al. .http://bib.oxfordjournals.org/ and It is worth noting that our analysis of unimodality data classification  rule for multimodal fusion and uncertainty calculation. The goal of TMI can be summarized in three points: (i) A modality’s classification result should be referenced to its confidence score; (ii) When the uncertainty of all modalities is high, the final prediction must be of low certainty, and vice versa; (iii) The final prediction and uncertainty score should be considered together and dynamically change according to each modality’s predicted outcome.To validate the efficacy of the uncertainty metrics, we visualized the changes in the performance of HTML using the in−/out-of-distribution (ID/OOD) samples. Here, we set the original samples as ID and the samples with added Gaussian noise as OOD. Specifically, we added Gaussian noise with varying standard deviations  but absent in normal and READ samples. In terms of miRNA expression features, hsa-mir-101-2 was identified as one of the prognostic miRNA signatures for COAD in a study by Lv et al. [Within the COAD biomarkers, LRMP has been identified as hypermethylated and downregulated in a subpopulation of highly mobile cells, known as MG cells, derived from a COAD cell line. These cells are distinguished by their migratory capacity and epithelial-mesenchymal transition attributes, which are indicative of the metastatic potential and malignancy of COAD. Tanaka et al.  conducteet al.  and geneet al. . In addiet al. , which eet al. , which e [et al.  and comp [et al. , as showv et al. . The tarhttp://bib.oxfordjournals.org/ and P value close to 0.001, which suggests that STC1 could be a biomarker for READ. Among the genes identified in the mRNA expression features, SPON1 is a coding gene that produces a product predicted to be secreted to the extracellular matrix and is involved in cell adhesion, a process that could be dysregulated in tumorigenesis. Supiot et al. [In terms of biomarkers for READ, STC1 is a member of the secretory glycoprotein family and is involved in processes such as apoptosis and inflammation. Its association with READ has been reported , and itst et al.  examinedt et al. . These ft et al. , KLK4 [5t et al.  and LHPPt et al. , all of In this study, we proposed a highly trustworthy multiomics learning framework (HTML), for personalized pancancer and cancer subtype diagnosis and prognosis. Through training on pancancer and cancer subtype datasets, HTML has the potential to seamlessly integrate multiple modalities of data, thereby enabling personalized medical diagnosis. This innovative technology is poised to overcome the existing barriers between pancancer diagnosis and cancer subtype determination, thereby achieving a comprehensive diagnosis pipeline. This pipeline is capable of processing one or more pieces of omics information from patients. It initially predicts the presence of cancer and the specific type of cancer and subsequently identifies the specific subtype of the patient’s cancer, which has the potential to integrate the entire diagnostic process into one model.Moreover, conventional models are unable to conduct sample adaptive analysis based on the unique data distribution characteristics of each sample and lack the ability to validate and verify the reliability of their own classification results. To address this challenge, we developed the HTML model, which leverages dynamic learning at both the feature and modality levels and Dirichlet uncertainty learning. This innovative model efficaciously integrates information across diverse modalities of data, enabling it to make cutting-edge classification predictions with remarkable accuracy, enabling the precise identification and diagnosis of cancer-causing genes for individuals, thereby making personalized treatment possible.HTML’s feature dynamic weight mechanism assigns a unique weight to each feature. This innovative approach enables us to identify potential biomarkers that can differentiate between various types of cancers and cancer subtypes, thereby laying the groundwork for further exploration of the biological mechanisms that underpin cancer development.Despite utilizing only DNA methylation, mRNA expression and miRNA expression data for our multiomics classification tasks, our HTML framework boasts impressive extensibility and can be customized to accommodate various types of data  by adjusting the model parameters accordingly. In this way, HTML represents a highly adaptable supervised multiomics classification framework with exceptional interpretability and extensibility.The multiomics input can be formulated as Suppose we have a single omic data input where The concept of feature dynamics can effectively address the issue of overfitting, in which the model tends to fit the noise in the input data rather than the underlying patterns . ThroughThrough the feature selection module, we obtained a better interomics feature representation In HTML, the original DNA methylation input features serve as the query vectors where where the alignment where After the feature dynamic module and methylation-guided attention, we obtained both the inner-omics feature representation To address the interactions among multiomics data and improve the classification task accuracy, we proposed a triple contrastive loss (TCL) function for learning comparisons among multiple modalities. This loss function brings together the distances among the representations where As with feature dynamic learning, the contribution of individual features varies in each modality toward the final classification outcome. This holds true for disparate modalities as well. To effectively account for each modality’s contribution to the final classification, HTML introduces a modality dynamic learning module to compute the model’s confidence (MCC) for different modalities. Take the DNA methylation feature where The Dirichlet distribution is a prevalent multivariate probability model used for random variable synthesis . It is ewhere It is noteworthy that each element in We introduce the Dempster–Shafer Theory of Evidence (DST) , which uwhere This supports the notion that the belief mass and overall uncertainty interact complementarily. In our experiments, the Dirichlet distribution allowed modelling of secondary probability and uncertainty beyond relying solely on the highest probability as the selected class . UtiliziOnce the determination of the belief mass, overall uncertainty and evidence is complete, a quantitative measure of the reliability of opinions can be obtained. Nevertheless, in multiple modality tasks, integrating the belief mass and overall uncertainty from various sources proves to be a challenging task. The integration rule should consider the uncertainty value instead of simply reconciling the belief mass values from different sources. Additionally, multimodal integration must account for conflicting opinions across diverse modalities. Given these requirements, we proposed the following fusion rule. The integrated uncertainty value is calculated as follows: The variable The cross-relation between the belief mass and the overall uncertainty distributes different weights for distinct modalities linked to their respective uncertainties. For instance, when the uncertainty value of modality 1 is higher than that of modality 2, the integrated where the operator The loss function for DST integration methods comprises the cross-entropy loss in each modality: where wherewhere We formulated our objective function for HTML by combining the triple contrastive loss, individual cross-entropy loss, and integrated loss. To further enhance the classification results, we incorporated uncertainty metrics and an We present a novel and highly personalized approach to cancer diagnosis and prognosisOur method outperforms static-architecture-based methods in various clinical tasks.Our method showcases the effectiveness of individualized treatment with a strong emphasis on explainabilityOur method incorporates uncertainty metrics to provide reliable indications of the model’s outputOur method contributes to advancing biological research by identifying potential biomarkersSupplementary_Information_bbad378Click here for additional data file.


Vibrio cholerae. V. cholerae transcriptome data include RNA-seq and microarray: microarray data mainly include clinical human and environmental samples, and RNA-seq data mainly focus on laboratory processing conditions, including different stresses and experimental animals in vivo. In this study, we integrated the data sets of both platforms using Rank-in and the Limma R package normalized Between Arrays function, achieving the first cross-platform transcriptome data integration of V. cholerae. By integrating the entire transcriptome data, we obtained the profiles of the most active or silent genes. By transferring the integrated expression profiles into the weighted correlation network analysis (WGCNA) pipeline, we identified the important functional modules of V. choleraein vitro stress treatment, gene manipulation, and in vitro culture as DNA transposon, chemotaxis and signaling, signal transduction, and secondary metabolic pathways, respectively. The analysis of functional module hub genes revealed the uniqueness of clinical human samples; however, under specific expression patterning, the Δhns, ΔoxyR1 strains, and tobramycin treatment group showed high expression profile similarity with human samples. By constructing a protein-protein interaction (PPI) interaction network, we discovered several unreported novel protein interactions within transposon functional modules.A large number of transcriptome studies generate important data and information for the study of pathogenic mechanisms of pathogens, including IMPORTANCE We used two techniques to integrate RNA-seq data for laboratory studies with clinical microarray data for the first time. The interactions between V. cholerae genes were obtained from a global perspective, as well as comparing the similarity between clinical human samples and the current experimental conditions, and uncovering the functional modules that play a major role under different conditions. We believe that this data integration can provide us with some insight and basis for elucidating the pathogenesis and clinical control of V. cholerae. Vibrio cholerae is still endemic in developing countries, such as Haiti and Zimbabwe, where adequate clean municipal water supply systems are scarce  and toxin-coregulated pilus (TCP) during V. cholerae colonization in animal models, as well as high expression of nutrient competition pathways, and discovered expression differences between animal models , and chromatin immunoprecipitation sequencing (ChIP-seq). DuPai et al.  (V. cholerae gene expression from the Gene Expression Omnibus (GEO) database. All RNA-seq samples were mapped to a recently inferred V. cholerae transcriptome derived from the N16961 reference genome by Kallisto (https://github.com/ShawnWx2019/WGCNA-shinyApp) pipeline to analyze the entire gene expression table and generate a co-expression network. The literature mining and manual curation workflow is shown in For initial raw data collection, we downloaded and curated all ve (SRA)  and liteKallisto . We treaThrough boxplot analysis, we found that there was a heterogeneous distribution of gene expression between the different platforms and therefore they could not be used for comparison . For exaV. cholerae from 2002 to June 2021, which contained expression data for 3,885 genes. Some genes were not included due to absence or low expression in the microarray cDNA template. We first examined the profile of the V. cholerae gene transcriptome in the context of the entire complex sample. The overall transcripts identified ~2,000 differentially expressed genes (DEGs) in V. cholerae genes . It is implied that most of the V. cholerae genes are active in different environments.In total, we obtained transcript profiles for 59 projects and 873 samples of V. cholerae, respectively. KEGG functional enrichment  and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The GO database  was founrichment  showed tvc2698, vca1031, vc0910, vc0911, and vc2699. The gene vc2698 (aspA), an aspartate ammonia-lyase, was identified in a previous study  was identified as phosphotransferase system (PTS) trehalose transporter subunit IIBC and vc0911 (treC) was identified as α,α-phosphotrehalase; both of these belong to the PTS system, which is essential for the colonization of V. cholerae, especially in the host  (rpoA (vc2571) (gyrB (vc0015) (thyA (vc0675) (recA (vc0543) , which is responsible for the formation of bacterial cell membranes.The internal reference genes are very important for transcriptional-level studies, and a good internal reference gene needs to meet a certain amount of expression and stable expression. The common internal reference genes selected for (vc0862) , rpoA (v(vc2571) , 17, gyr(vc0015) , 19, thy(vc0675) , and rec(vc0543) . Under a(vc0543) . These g(vc0543) . We alsocriteria . Gene vcin vitro culture samples, at about 75%, with the remaining portion consisting of in vitro stress (9%), genetic manipulation (6%), and in vivo samples (9%). Group 2 is dominated by in vitro culture (35%) and in vitro stress samples (46%), with the remaining portion consisting of in vivo (11%) and genetic manipulation samples (8%). Group 3 is dominated by genetic manipulation samples, at about 66%, with the remaining portion consisting of in vitro culture (23%), in vitro stress (10%), and in vivo samples (1%). Group 4 contained no in vivo samples and consisted of in vitro culture (63%), genetic manipulation (31%), and in vitro stress samples (6%). The co-expressed gene network in V. cholerae transcripts was revealed by the WGCNA pipeline, which distributed the filtered genes into 6 modules  and the clustering displayed in the reduced-dimensional t-SNE plot . Group 1 modules ; the cor modules : group 1V. cholerae can be artificially divided into the main categories of stress conditions, in vitro culture, in vivo host, and gene manipulation. Although these samples were dispersed within groups by hierarchical clustering, we wanted to investigate whether treatment conditions within the same group correlated with gene expression patterns. After removing the different sample types from each group, we calculated the correlation between modules and groups  networks from hub genes identified in WGCNA using STRING , a curreV. cholerae from both platforms through Rank-in and thus constructed a gene co-expression network for V. cholerae using WGCNA. This cross-platform data integration allows us to bring a more diverse perspective to the study of the V. cholerae transcriptome in response to different environmental changes, while also incorporating the most important clinical human samples in pathogenic bacteria research. Our integrated data set includes the shortcomings of currently used transcriptional internal reference genes under certain specific experimental conditions in a holistic perspective, while providing several genes that are relatively consistently expressed under all experimental conditions for reference purposes. Also, after removing batch effects and inter-platform heterogeneity, we identified auto-clustering among different samples, analyzed them, and constructed gene co-expression networks, from which functional modules and hub genes with high relevance to the samples were screened and identified. We also constructed PPI networks to predict the possible existence of unstudied gene interactions. Interestingly, similar results can be obtained by integrating the data through the Limma R package normalizedBetweenArrays function .We have successfully integrated transcriptomic data of Although the logic for integrating data differs between the two pipelines, the main highly correlated modules are quite similar, such as group 2 with the turquoise module and group 3 with the blue module . The hub genes within the modules are shown as a Sankey diagram, where the turquoise and blue modules are nearly identical to the average gene in both merging methods, and the brown module is not as similar to the green module .V. cholerae treated with different stresses, suggesting that V. cholerae adapted to challenges by regulating DNA-level changes and its membrane structure under different stress conditions. The functions enriched by hub genes within the blue module are mainly focused on chemotaxis, signal transduction, and metabolism-related pathways. Approximately 61% of the group 3 samples strongly associated with the blue module are from genetically manipulated V. cholerae which were subsequently re-stressed or entered the host, making it difficult to distinguish whether genetic manipulation or another factor is the main effector. The functions enriched by the hub genes in the brown module are focused on cytoplasmic, signal transduction, and metabolic pathways of bacterial responses to different environments. About 63% of the samples in group 4, which is strongly associated with the brown module, are from V. cholerae cultured in vitro, suggesting that V. cholerae cultured under different conditions in vitro regulate metabolic pathways that are adapted to different environments through signal transduction.The functions enriched by hub genes within the turquoise module mainly focus on DNA-level changes such as transposases, DNA-mediated transposons, acetyltransferases, and important membrane components. About 46% of the samples in group 2, which was strongly associated with the turquoise module, were from V. cholerae. Horizontal gene transfer is an important means for V. cholerae to rapidly acquire adaptive traits such as antibiotic resistance. Notably, V. cholerae possesses the ability of natural transformation on the surface of chitin in the natural water environment to take up exogenous DNA . However, in addition to its regulatory role in ROS resistance, oxyR1 also acts on the two-component regulatory system vc1084-vc1086, CTX phage production . Considering that the ΔoxyR1 strain was similar to human samples with or without H2O2, a correlation between the ΔoxyR1 strain and human samples in terms of ROS resistance under the group 3 hub gene expression pattern can be ruled out, and it is possible that other downstream genes play a primary role. Tobramycin is a class of aminoglycoside antibiotics which cause double-stranded DNA breaks in V. cholerae. When radD or recBCD is absent, previous studies  is an important class of regulatory proteins in bacteria which affect the transcriptional functions of RNA polymerase by binding to DNA. H-NS is now known to have important functions within sistance –31. In ccholerae , so it i studies  have sho studies , 35 haveV. cholerae, including infant mice, infant rabbits, Drosophila, adult mice, and amoebae. However, the different animal models only represent specific aspects of V. cholerae pathogenesis and do not fully simulate its real situation in human infections. In our study, we also found that different in vivo samples from different hosts are naturally assigned to different groups, where human samples are still unique. This implies that we still need to develop animal models which more closely resemble human infections to advance research related to V. cholerae.Many animal models are currently used to study Here, we explain the reasons for the method used within the manuscript. For RNA-seq expression data extraction, given the large number of samples included in this analysis, we chose Kallisto because it has the fastest processing speed, in line with the previous publications. For integration of microarray and RNA-seq, we used three currently published integration methods, including Rank-in, the Limma R package normalizedBetweenArrays, and TDM ; Of thesAfter integration using the Limma R package normalizedBetweenArrays function, the hierarchical clustering results showed that approximately 80% of the samples were grouped into the same group, exhibiting anomalous sample clustering . We therefore conducted a WGCNA analysis of the integration data from the Limma R package normalizedBetweenArrays function pipeline using the clustering grouping information from the Rank-in pipeline. The results showed that more functional modules were identified than in the Rank-in pipeline, but the modules in red, yellow, brown, and turquoise still possessed some correlation. The correlations between modules and groups in both pipelines were consistent, i.e., group 2 with the turquoise module and group 3 with the blue module. Gene composition within both modules is shown in a Sankey diagram .The data distribution of expression intensity processed by each method was checked and compared between array and RNA-seq technologies. The raw data without processing demonstrated two peaks in microarray but one completely deviating peak in RNA-seq . In contrast to the large difference in the uncorrected method, the Limma R package normalizedBetweenArrays function compresses data from both platforms into one peak, and Rank-in tries to eliminate deviation peaks . For sample clustering, Rank-in presents a more uniform grouping, while the Limma R package normalizedBetweenArrays function has most of the samples clustered in group 1 . Combining the expression distribution, sample clustering, and independence between functional modules after integration of the two pipelines suggests that Rank-in may be more applicable to our data set than the Limma R package normalizedBetweenArrays function.in vivo data with the in vitro data from the integrated data set. In vitro control samples from the three hosts were designated the in vitro group, and clinical human samples, infant rabbit samples, and Drosophila samples were divided into three other groups. The results showed that the functional modules enriched were not the same between hosts . These samples were simply divided into in vivo and in vitro data for WGCNA analysis, where the blue module exhibited a significant negative correlation with the in vivo group and the gray module exhibited a significant positive correlation . By comparing the KEGG-annotated V. cholerae infection-associated genes with the gray module genes, fewer genes were found to be shared, in agreement with other human data. Namely, the virulence-associated-genes did not seem to be significantly upregulated in the human gut.WGCNA analysis was performed using V. cholerae transcriptome derived from the N16961 reference genome (All RNA-seq data were downloaded from the Sequence Read Archive (SRA)  and litee genome  using Kae genome . The twoe genome . This reFor Rank-in, the RNA-seq TPM generated by Kallisto was combined with the gene expression from the microarray into a gene expression matrix. Experimental grouping information for the corresponding samples was collated into a phenotype matrix. Both files were submitted to the Rank-in website to perform data integration.For the Limma R package normalizedBetweenArrays function, the RNA-seq count generated by Kallisto was combined with the gene expression from the microarray into a gene expression matrix. The entire gene matrix was homogenized in the R environment using the normalizedBetweenArrays directive in the Limma package.https://github.com/ShawnWx2019/WGCNA-shinyApp) by entering the gene expression matrix. This process consists of sequential calculation of a Pearson correlation matrix, adjacency matrix with power β = 3, and ultimately a topological overlap matrix (TOM)   from norPredicted pathway annotations and gene functional knowledge were derived from the NCBI Biosystems database as well as the DAVID, GO, and KEGG databases –41.–SRA and GEO accession numbers and information on all included samples can be found in Table S1 in the supplemental material. The integral gene expression matrix is provided in Tables S3 and S4. The PC list table is provided in Table S2. Sample clustering group information list table is provided in Table S5. All data analysis and figure generation was performed using the Linux and R programming language. All data relevant to the study are included in the article or have been uploaded as supplementary information.