Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity

doi:10.21203/rs.3.rs-2852103/v1

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Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity

https://doi.org/10.21203/rs.3.rs-2852103/v1

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published 05 Apr, 2024

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Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches on common inflammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-specific meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common inflammatory mechanisms fostered by the perturbed expression profile of pathogenic cell types. Assessment of gene overlaps between both diseases revealed significant overlaps between up- (n = 170, P-value = 6.07×10^–65) and down-regulated (n = 49, P-value = 7.1×10^− 7) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses confirmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel findings through whole transcriptomic analyses characterize the inflammatory commonalities between psoriasis and obesity implying the assessment of several expression profiles that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.

Biological sciences/Genetics/Gene expression

Biological sciences/Immunology/Autoimmunity

Psoriasis is a chronic inflammatory skin disease that affects 2–4% of the world’s population, triggered by complex interactions between genetic and environmental factors with an estimated participation at 70 and 30% respectively [1]. Psoriasis is nowadays regarded as an autoimmune disorder due to the implication of immune-related genes and pathways in its pathogenesis as unveiled by genetic association approaches, depicting the importance of the Human Leukocyte Antigen (HLA) locus as well as inflammatory pathways that interplay between the cellular heterogeneity of psoriasis [2]. The molecular variation of the cutaneous inflammation, combined with the circulating cytokines in contrast to the systemic nature of psoriasis expand the inflammatory cascade throughout secondary organs and contribute to the development of comorbid diseases, with major examples including Psoriatic arthritis (PsA), Inflammatory Bowel Diseases (IBD), cardiovascular diseases (CVD) and metabolic syndrome [3].

The escalating prevalence of psoriasis in westernized societies has highlighted the importance of the environmental exposures [4] that interplay with the relatively stable genetic basis of the disease, parallelly augmenting the related comorbidities [3]. In-depth characterization of the underlying mechanisms that govern the above diseases has successfully disentangled shared pathogenic features leading to the establishment of common therapeutic approaches in the clinical routine, as is the case with PsA and IBD [5].

A notable example of an arising psoriasis comorbidity is obesity, a metabolic-related disease characterized by the abnormal fat accumulation presenting a severe health risk; hypoxia induced by the dysfunction of the adipose tissue during adipocyte hypertrophy [6] leads to the extensive infiltration of immune cells and the secretion of pro-inflammatory cytokines and adipokines [7]. This inflammatory milieu is postulated to exacerbate the psoriatic inflammation. Several epidemiological studies have displayed significant associations between obesity and psoriasis prevalence as well as severity [8], while dietary patterns associated with obesity aggravate the maintenance psoriasis phase through keratinocyte activation and hyperproliferation [9]. Despite the abundance of literature suggesting common perturbations between both diseases, systematic approaches on such putative mechanisms during the inflammation have not been adequately explored and, instead, focus on the role of external exposures and the correlated microbial functions [9].

To illustrate consensus mechanisms between psoriasis and obesity, we conducted a meta-analysis of publicly available whole transcriptome sequencing datasets exploring the expression perturbations observed in both diseases. We performed two disease-specific meta-analyses via the Fisher’s combined probability test and identified differentially expressed genes (DEGs), thus unveiling shared perturbed genes and mechanisms. In addition, we explored consensus co-expression modules between the two comorbid diseases to describe shared molecular mechanisms that orchestrate the inflammatory response, regardless of rigorous statistical thresholds.

Identification of eligible datasets

We considered all bulk RNA-sequencing datasets that examined the whole transcriptome between adult psoriasis and obesity patients in comparison to healthy individuals. We systematically screened the available datasets on the Gene Expression Omnibus platform from inception to September 3rd, 2022. Keywords included in our search strategy included “Psoriasis” and “Obesity” aiming to capture most available datasets that referred to gene expression studies from the respective inflamed biopsies for each disease, namely skin and subcutaneous adipose tissue (SAT).

Pre-processing of the RNA-seq datasets

Datasets were pre-processed using the same tools, nevertheless with respect to the different experimental protocols and thus divergent handling each dataset required, such as paired-end or single-end sequencing and strand specificity. Raw sequence data of the appropriate samples for all identified datasets were downloaded from the NCBI Sequence Read Archive and converted to FASTQ format using the SRA toolkit. The quality assessment of unprocessed sequence reads was conducted with FastQC, while Trimgalore was employed to facilitate the elimination of potential contaminants and low-quality reads. The STAR aligner (version 2.5.7) was used to perform alignment of the reads to the GRCh38 reference genome. Prior to gene expression quantification, strand specificity of each RNA-seq experiment was assessed with the infer_experiment.py command of the RSeQC package to properly handle each dataset. The featureCounts read summarization program implicated in the SubRead package facilitated the summarization of the mapped reads and the formation of raw count matrices. Reads mapping to multiple loci were discarded from downstream analyses.

DEGs were assessed for each datasets using a negative binomial distribution implicated in the DESeq2 R package, fostered by the independent filtering algorithm. Technical replicates were handled through DESeq2.

Disease-specific meta-analyses

Yoon and colleagues reported that P-value combination robust approaches outperform the majority of current approaches, including the effect size aggregation, capable of detecting incomplete associations [10]. Raw P-values for each disease-specific dataset as derived from the DESeq2 approach were loaded and meta-analyzed with the Fisher’s combined probability test and consequently corrected for multiple corrections using the Benjamini-Hochberg procedure. We considered all genes that passed the independent filtering algorithm of DESeq2 for each disease-specific dataset. Summary fold change for each gene was calculated based on the mean fold change of all incorporated datasets.

Regardless of the disease-specific meta-analysis, genes were considered as differentially expressed according to their adjusted P-value ≤ 0.01 and mean log₂|Fold Change| (log₂|FC|) ≥ log₂(1.5). Gene set enrichment analysis (GSEA) was conducted to depict the concordant perturbed mechanisms in both diseases in the kyoto encyclopedia of genes and genomes (KEGG) pathways database [11]. Genes were ranked according to their log₂FC values. The P-value of the enrichment score of the GSEA was calculated via a permutation test and controlled for multiple comparisons with the Benjamini-Hochberg method. Hypergeometric tests were incorporated to assess significant overlap between up- and down-regulated genes. Putative pathways of the shared genes were additionally explored with over-representation analysis in the KEGG pathways database, with the P-values calculated from hypergeometric distribution and consequently corrected for multiple comparisons with the Benjamini-Hochberg method. All functional enrichment analyses were conducted with the clusterProfiler R package [12].

Consensus weighted co-expression network analysis

Despite the robust list of deregulated genes a differential gene expression analysis is able to depict, the complexity of such phenotypes requires further comprehensive analyses in order to capture the multi-layered transcriptional regulation and putative inflammatory mechanisms of both diseases. We performed a consensus co-expression analysis utilizing the weighted gene co-expression network analysis (WGCNA) approach to unveil co-expression similarities between coordinately expressed genes in both disease datasets [13]. To reduce the inherited heterogeneity in high-throughput experiments, we incorporated only paired-end sequencing datasets in our WGCNA approach. We then performed cross-study normalization with the Combat-seq approach on the top 75% genes for each dataset according to their variance, using the dataset accession code as a batch effect. The combined, disease-specific count matrices were transformed with the variance stabilizing transformation implemented in the DESeq2 package, while Principal Component analysis (PCA) allowed to explore the efficacy of the previous batch corrections and possible detection of outliers. The batch-corrected, transformed reads were then used for consensus co-expression network analyses with the WGCNA package. The soft thresholding power β was determined at 12 based on the approximate scale-free topology. Network adjacencies were calculated in accordance with the selected β power with the biweight midcorrelation (bicor) method due to the multiple expected outlier measurements, and consequently transformed into a scaled, comparable across psoriasis and obesity signed Topological Overlap Matrix (TOM). The consensus TOM was calculated according to the component-wise minimum of the disease-specific TOMs and subsequently sent to hierarchical clustering to identify the corresponding modules. The deepSplit parameter was set at 4 and the minModuleSize at 20.

For each derived consensus module, correlations between each disease status were calculated using bicor; summarization of both module-trait relationships was conservatively depicted by choosing the lowest absolute value with the same sign in both disease-specific sets. If the disease-specific correlations displayed opposite bicor rho values, the summarized module-trait relationship was set at 0. Each consensus module was further submitted to downstream analyses including functional enrichment as described above, discriminative ability of each module between each disease status through the area under the roc (AUC) curve computing c-statistic and the respective 95% confidence intervals (95%CI), visualized according to the calculated consensus eigengene-based connectivities in the Cytoscape app [14] and clustered with the molecular complex detection (MCODE) algorithm [15].

Datasets included in the study

In total, our systematic search identified 9 datasets with 4 referring to 76 psoriatic patients and 5 referring to 76 obese patients, in contrast to the 75 and 55 healthy individuals respectively (Table 1). Due to the unavailability of exclusive approaches on obesity, we selected expression arrays that included metabolically healthy obese (MHO) patients and lean individuals, regardless of the interventions a specific study would aim to assess, since most psoriasis datasets did not provide sufficient clinical data on the metabolic status of the included patients. Selected samples from both diseases are available in the Supplementary Table 1.

Table 1

Datasets included in our study.
Study	Accession ID	SRA	Platform	Tissue	Cases	Controls
Psoriasis
Xue et al., 2018 [16]	GSE114286	SRP145260	Ion Torrent Proton	Skin	18	9
Gupta et al., 2016 [17]	GSE74697	SRP065812	Illumina HiSeq 2500	Skin	18	16
Keerman et al., 2015 [18]	GSE66511	SRP055813	AB 5500xl Genetic Analyzer	Skin	12	12
Tsoi et al., 2019 [19]	GSE121212	SRP165679	Illumina HiSeq 2500	Skin	28	38
Obesity
Fuchs et al., 2021 [20]	GSE156906	SRP278883	Illumina NovaSeq 6000	SAT	28	14
Fisk et al., 2021 [21]	GSE162653	SRP295864	Illumina HiSeq 2000	WAT	10	10
-	GSE110729	SRP132990	Illumina HiSeq 2000	WAT	13	15
Cifarelli et al., 2020 [22]	GSE152991	SRP268322	Illumina NovaSeq 6000	SAT	15	11
Rey et al., 2021 [23]	GSE166047	SRP304398	Illumina NextSeq 500	SAT	10	5
Abbreviations: SRA, Sequence read archive; SAT, Subcutaneous adipose tissue; WAT, Subcutaneous white adipose tissue.

Single gene meta-analysis characterizes the inflammatory milieu in psoriasis and obesity

Our primary goal was to identify the transcriptional landscape of both psoriasis and obesity using a late-stage meta-analysis approach with the Fisher’s combined probability test on the dataset-specific summary statistics, as derived from the negative binomial distribution. Meta-analysis of psoriasis datasets depicted 9 292 deregulated genes with a False Discovery Rate (FDR) P-value ≤ 0.01 and log₂|FC|≥log₂(1.5), where 2 154 were over-expressed and 7 138 repressed (Supplementary Table 2; Fig. 1a). Contrary to the abundance of DEGs in psoriasis, the obesity meta-analysis accumulated a total of 637 significant DEGs (532 up-regulated, 105 down-regulated) according to the same FDR and fold change criteria (Supplementary Table 3; Fig. 1c).

We next employed GSEA for each disease-specific meta-analysis according to the KEGG pathways database [11] to gain additional insight into the biological mechanisms from the expression profile of both comorbid diseases. Our analyses illustrated the highly over-expressed inflammatory mechanisms present in both inflamed tissues, with major activated pathways referring to Interleukin (IL)-17 signaling in psoriasis (Fig. 1b; Supplementary Table 4) and NF-κB activation in obesity (Fig. 1d; Supplementary Table 5).

The above similarities prompted us to investigate the pathway commonalities by comparing the Normalized Enrichment Scores (NESs) between the shared KEGG pathways terms. We confirmed the shared inflammatory expression present in both diseases, associated with the activation of both NF-κB signaling and T helper (Th)17 differentiation pathways, while repressed pathways referred to metabolic processes (Fig. 1e). Our comparison further uncovered the differentiated activation profile of insulin secretion and translational pathways between the comorbid diseases (Fig. 1e).

Significant overlap between DEGs reveals common mechanisms

To accurately describe the commonalities of the inflammatory milieu in both SAT and lesional psoriatic skin, we assessed the overlap of the deregulated genes from both meta-analyses and explored their functional profile via ORA on the KEGG pathways database. The hypergeometric test revealed significant overlaps regarding both up-regulated (n = 170, P-value = 6.07×10^–65) (Fig. 2a) as well as repressed (n = 49, P-value = 7.1×10^− 7) genes (Fig. 2a), however overlap tests were non-significant in the opposite direction (Supplementary Fig. S), establishing thus the molecular similarity of psoriasis and obesity. Functional enrichment of both perturbed categories, consistently with the prior GSEAs, unveiled an abundance of leukocyte and NF-κB activation related terms at the bulk biopsies regarding the shared up-regulated genes (Fig. 2b), however enrichment results were non-significant regarding the down-regulated genes. Notably, enrichment of the above pathways was accompanied by the absence of the central deregulated molecules in psoriasis, such as Tumor Necrosis Factor (TNF) and IL-6, IL-17 and IL-23. Nevertheless, several molecules associated with the Th17 differentiation pathway were present in the over-representation analysis, with exemplars of the up-regulated TBX21 transcription factor (TF) and the IL4I1 gene, as well as the down-regulated RORC TF (Fig. 2a). Identification of the enriched pathways derived from our functional analyses delineated the above gene list, illustrating their functional role in the disease progression through the activation of the pathogenic cell types and suggesting common mechanisms of the inflammatory maintenance.

Consensus co-expression network analysis

We next applied cWGCNA towards the unveiling of putative similarities between both disease expression sets through a post-hoc clustering approach [13]. The advantage of the cWGCNA approach lies in the investigation of co-expression modules that are relatively preserved amongst the independent datasets, allowing thus a cleaner functional interpretation of the corresponding modules [24].

We assessed only paired-end sequencing reads in our cWGCNA approach to reduce the inherent experimental heterogeneity; Principal Component Analysis (PCA) visualization following batch effects correction highlighted the SRP132990 obesity dataset as potential outlier and as thus excluded from downstream analyses (Supplementary Fig. 2). Ultimately, our cWGCNA analysis incorporated the psoriasis SRP065812 and SRP165679 (n_patients=46, n_healthy=54; Supplementary Fig. 3) datasets and SRP278883 and SRP304398 (n_patients=38, n_healthy=19; Supplementary Fig. 4) datasets. Intersect of both disease-specific datasets resulted in the utilization of 44.485 transcripts. By applying the cWGCNA in our merged matrices using 12 as a soft threshold power (Supplementary Fig. 5) and bicor to eliminate potential outliers, we identified 48 different co-expression modules (Supplementary Fig. 6) with a number of genes ranging from 23 to 1 625, with 33 991 transcripts falling into the grey module (Supplementary Table 6).

We then calculated the bicor of the consensus eigengenes, identified from each consensus co-expression module, with the expression profiles of both comorbid diseases (Supplementary Table 7). Conservative summarization of both trait-module relationships uncovered several consensus module eigengenes with the same bicor sign between psoriasis and obesity, while some of them displayed an opposite bicor pattern (Fig. 3a). KEGG pathways enrichment analysis on the respective eigengenes further enabled us to accurately describe the pathological commonalities as well as differences between both diseases. Specifically, modules (MEs) 11 and ME3 showed the largest positive correlation for psoriasis (P-value_psoriasis=7.29×10^–40; P-value_psoriasis=6.89×10^–37), while ME30 and ME37 (P-value_obesity= 1.02×10^–10; P-value_obesity=1.3×10^− 9) were positively associated with obesity (Fig. 3a). Regarding the significant psoriasis modules, ME11 is associated with DNA replication and mitosis, while transcripts in the ME3 module refer to the extended inflammatory pathways present in lesional biopsies. However, both obesity-related modules were unable to depict significant associations with BP terms after the Benjamini-Hochberg correction, with the ME30 module incorporating genes implicated in cell activation (IL-7, CCN2, IL18) and tissue remodeling (PAPLN, MMP7), while ME37 module incorporated genes that participate in ion transport (TRPM2, GRIN2B). Strikingly, ME30 showed the largest negative association to psoriasis (P-value_psoriasis=7.7×10^–40), followed by the ME13 module (P-value_psoriasis=5.7×10^–35) associated with metabolic pathways of fatty acids. Similar results were derived from the negative correlations in obesity, where both ME4 (P-value_obesity= 2.9×10^− 9) and ME40 (P-value_obesity=1.5×10^− 8) referred to metabolic pathways and response to insulin (Fig. 3b), confirming thus our single-gene meta-analysis results. In general, the derived modules were highly preserved (Fig. 3c) with a high-density value D = 0.74 indicating the shared inflammatory profile between both comorbid diseases.

We then focused on the consensus positive correlations on the top 3 consensus modules and investigated possible overlaps to the shared DEGs list; significant commonalities were detected on the ME3 (n = 55, P-value = 8.83×10^–56) and ME45 (n = 5, P-value = 1.42×10^− 7) modules. However, the ME3 module also displayed the highest discriminative ability of all consensus modules (Supplementary Table 8), implying the investigation of its co-expression network. We visualized the consensus TOM of ME3 (threshold = 0.04) and interrogated it for the presence of the shared DEGs (Fig. 3d). Identification of the top clusters according to the MCODE algorithm [15] incorporated in the Cytoscape app [14] confirmed the presence of several highly over-expressed genes as observed in the single-gene meta-analysis related to the activity of Th17 cells, including CCR5 and IL4I1.

In this study, we provide a comprehensive analysis of the molecular similarities between psoriasis and obesity through meta-analysis of hypothesis-free transcriptomics data. We show that the inflammation in both tissues is associated with the perturbed secretion of pro-inflammatory cytokines and related pathways, with the Th17 profile serving as the major pathogenic axis. Specifically, our single-gene meta-analyses documented robust evidence of the inflammatory similarities between psoriasis and obesity regarding both the activated pathways (Fig. 1e) and shared deregulated genes (Fig. 2a). Similarly, our consensus network-based approach confirmed our previous results through the orchestrated co-expression of several proinflammatory molecules, while hub nodes were also associated with the pathogenic Th17 differentiation axis (Fig. 3d). Our extensive analysis of transcriptomics data illustrates the molecular etiology underlying the co-occurring rate between psoriasis and obesity deciphering the consensus inflammatory milieu.

Enrichment analyses from psoriasis and obesity unveiled an extended list of KEGG pathway terms referring to inflammatory pathways that constitute the central activity of each inflamed biopsy. In specific, the psoriasis GSEA depicted the overexpression of activated inflammatory pathways, including IL-17, TNF and NF-κB signaling, while fatty acid metabolism and insulin secretion pathways were significantly repressed (Fig. 1b). Our results come in concordance with the meta-analysis conducted by Tian and colleagues [25], where the hallmark cytokines in lesional biopsies of psoriasis patients were significantly represented in the pathway analyses, despite the respective cytokines were indistinguishable in the psoriasis DGE list (Fig. 1b; Supplementary Table 2). Additionally, elevated NESs regarding the obesity GSEA were observed in the NF-κB signaling, TNF cytokine production and Th17 differentiation, while pathways regarding the metabolism of fatty acids and translational activities were under-expressed (Fig. 1d). Regulation of the inflammatory pathways in the SAT by the NF-κB activation has been extensively characterized, with both stimulators and induced molecules linking obesity to the decreased fatty acid metabolic activity [26–28].

Comparison of the NESs between the shared KEGG pathways terms gave additional insight to the inflammatory profile of each inflamed tissue as well as the molecular discrepancies between each disease. The NES comparisons highlighted the inverted transcriptomic profile of translational pathways and insulin secretion (Fig. 1e); translational activities induced by cell growth and differentiation, enhanced by the inflammatory profile, are dysregulated during the hyperproliferation of keratinocytes in psoriasis [25, 29] and the bioenergetic burden from the accumulated body fat in obesity, a discrepancy hypothesized to compile a homeostatic protective mechanism in MHO individuals [30, 31]. Consistently, MHO patients exhibit an increased insulin secretion in comparison to lean [32] and insulin resistant [33] individuals, an expression profile postulated to represent a transient state towards the exacerbation of metabolic complications and accompanied cardiovascular disorders [34].

When examining the consensus modules derived from the co-expression network analyses between psoriatic skin and SAT of MHO patients, we identified 48 modules with diverse bicor patterns (Fig. 3a). Despite the underlying biological discrepancies between the SAT and lesional skin, the inflammatory profile present in each biopsy governed the co-expression networks as well, documenting a relatively high preservation value (Fig. 3c). These results come in agreement with the single-gene meta-analyses, concurrently highlighting the perturbed activation of inflammatory processes and pathogenic cell types (Fig. 3b). Cell type deconvolution pipelines could pinpoint the exact cellular profile of each inflamed biopsy, guiding future research towards the characterization of the cell-derived molecular similarities and consensus mechanisms [35]. Such analyses will help in comprehending the exact mechanisms that aggravate the inflammatory cascade in obese psoriatic patients, as well as obstruct the reduction of inflammatory indicators during pharmacological treatment [36].

Deconvolution pipelines could be also employed to delineate the perturbed Th17 differentiation pathways observed throughout all our single-gene meta-analyses (Fig. 1e), comparison of the shared deregulated genes (Fig. 2b) and consensus co-expression networks (Fig. 3b). Indeed, the Th17-related secretome has been suggested as the pathogenic link between obesity and several autoimmune diseases [37, 38], with however limited molecular data in psoriasis [9, 39–42]. Through the thorough examination of the shared deregulated genes from our single-gene meta-analyses (Fig. 2a), we identified 2 TFs with an established role during Th17 differentiation and activation, including the Th17-master regulator RORC and Th17-repressor TBX21 [43], while the IL4I1 oxidase displays a significant upregulation in IL-17 secreting Th17 cells [44]. Despite the absence of the RORC gene in a correlated module (Supplementary Table 6), both IL4I1 and TBX21 fell in positively consensus correlated consensus modules (ME3, ME8 respectively; Fig. 3a), with the former further identified as a hub gene in the co-expression network with the highest discriminative ability (Fig. 3d). Our thorough molecular examination implies the opposing self-control of the pathogenic Th17 cells in the biopsies of inflammatory disorders through the over-expression of the IL4I1 oxidase and TBX21 TF, as well as the downregulation of RORC, results further identified in transcriptomic datasets of psoriasis patients not included in our analyses [45, 46]. Elucidation of the complex molecular mechanisms that orchestrate the pathogenicity of Th17 cells shall facilitate the interpretation of the heterogeneous response profile, as implied via the unresponsive profile of obese psoriasis patients receiving anti-TNF therapies [47] and consequent weight gain [48], as well as the adequate response profile of patients receiving anti-IL12/23 and anti-IL17 drugs [35, 47].

There are some constraints to our study. Our approach incorporated a limited number of patient metadata due to the unavailability of additional information in each selected dataset. The presence of obese psoriasis patients in both our single-gene meta-analyses and consensus networks might have further affected our derived results. Nonetheless, our purpose was not to provide an analytical representation of the molecular profile of each disease, rather than compare their transcriptomic commonalities. Additionally, we explored the commonalities between two distinct patient groups with a discrete, however similar biological profile; extensive scrutinization of the transcriptomic landscape of obese compared to lean psoriasis patients is deemed indispensable to pinpoint the potential mechanisms of the high comorbidity prevalence. Lastly, validation of the above results in external datasets, as well as targeted quantification of central genes derived from our consensus analyses (Fig. 3d) would provide significant insights into each disease etiology and further illustrate the putative immune- and cell-related drivers.

To our knowledge, this is the first systematic analysis that explores the molecular similarities between psoriasis and obesity, despite their increasing comorbid ratio and related outcomes. We document the transcriptomics signals that aggravate the inflammatory pathways and suggest mechanisms that might explain the delayed drug response reported in obese psoriasis patients. Future work should focus on the disease course of obese psoriasis patients during pharmacotherapy to unveil the perturbed expression signals that impede the amelioration of psoriatic symptoms and provide clear guidelines to facilitate the development of precision medicine approaches in the clinical routine.

Acknowledgments

None.

Data availability statement

All, raw and processed from the corresponding authors, datasets included in our study are available from the Gene Expression Omnibus database repository (https://www.ncbi.nlm.nih.gov/geo/, accessed on 3 September 2022).

Conflicts of interest

The authors declare no conflicts of interest.

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There is NO conflict of interest to disclose. The authors declare no conflict of interest.

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Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity

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Journal Publication

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Abstract

Figures

Introduction

Materials and methods

Identification of eligible datasets

Pre-processing of the RNA-seq datasets

Disease-specific meta-analyses

Consensus weighted co-expression network analysis

Results

Datasets included in the study

Single gene meta-analysis characterizes the inflammatory milieu in psoriasis and obesity

Significant overlap between DEGs reveals common mechanisms

Consensus co-expression network analysis

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1