Abstract Psoriasis vulgaris is the most common form of the four clinical types. However, early diagnosis of psoriasis vulgaris is difficult due to the lack of effective biomarkers. The aim of this study was to screen potential biomarkers for the diagnosis of psoriasis. In our study, we downloaded the original data from [32]GSE30999 and [33]GSE41664, and the autophagy-related genes list from human autophagy database to identify differentially expressed autophagy-related genes (DERAGs) by R software. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for DERAGs. DERAGs were validated by the other four databases ([34]GSE13355, [35]GSE14905, [36]GSE6710, and [37]GSE55201) to screen biomarkers with high diagnostic value for the early diagnosis of psoriasis vulgaris. Finally, DERAGs were verified in our clinical blood samples by ELISA. A total of 12 DERAGs were identified between 123 paired non-lesional and lesional skin samples from patients with psoriasis vulgaris. GO and KEGG enrichment analysis indicated the TORC2 complex was more enriched and the NOD-like receptor signaling pathway was mostly enriched. Three autophagy-related genes (BIRC5, NAMPT and BCL2) were identified through bioinformatics analysis and verified by ELISA in clinical blood samples. And these genes showed high diagnostic value for the early diagnosis of psoriasis vulgaris. We identified three autophagy-related genes (BIRC5, NAMPT and BCL2) with high diagnostic value for the early diagnosis of psoriasis vulgaris through bioinformatics analysis and clinical samples. Therefore, we proposed that BIRC5, NAMPT and BCL2 may be as potential biomarkers for the early diagnosis of psoriasis vulgaris. In addition, BIRC5, NAMPT and BCL2 may affect the development of psoriasis by regulating autophagy. Subject terms: Diagnostic markers, Skin diseases __________________________________________________________________ Psoriasis is a chronic inflammatory skin disease that affects 2–3% of the world’s population^[38]1. At present, effective treatment of the disease is limited, and it relapses easily, which usually leads to a significantly decreased quality of life, increased psychological stress, and obvious economic burden^[39]2. Of the four clinical types, psoriasis vulgaris is the most common form, with erythematous and scaly plaques as its main manifestations^[40]3, and accounts for nearly 90% of all psoriasis conditions^[41]4. Signs and symptoms of psoriasis vulgaris are sometimes atypical and always require a differential diagnosis of eczema or pityriasis rosea. Several studies have been performed to elucidate the molecular mechanisms underlying psoriasis^[42]5,[43]6. However, only a few studies have reported biomarkers for the diagnosis of psoriasis vulgaris. In this study, we attempted to identify potential biomarkers for the diagnosis of psoriasis vulgaris based on bioinformatics analysis. One possible method is to search the GEO database for potential diagnostic biomarkers of psoriasis vulgaris, as large-scale free datasets in GEO currently make the analysis feasible. To date, many studies have focused on the potential mechanisms involved in psoriasis. For example, Zeng et al. reported that some transcription factors could play an important role in the pathogenesis of psoriasis^[44]5; Deng et al. found some key regulators of psoriasis^[45]6; and our team also identified 13 key apoptosis-related genes associated with psoriasis^[46]7. Autophagy is a cellular process that involves the degradation and recycling of cellular components, such as damaged proteins, organelles, and other cytoplasmic contents^[47]8. Autophagy has been implicated in a number of diseases including psoriasis^[48]9. For example, LncRNA MEG3 enhance autophagy by PI3K/AKT/mTOR signalling pathway to inhibit psoriasis-like skin inflammation^[49]10.In addition, aurora kinase A promotes the psoriasis-related inflammation by regulating autophagy^[50]11. Therefore, understanding the mechanisms of autophagy and its regulation may have important implications for the occurrence and development of psoriasis. However, there are few studies focused on autophagy-related genes in the diagnosis of psoriasis. Exploring the potential autophagy-related genes of psoriasis will provide us potential biomarkers for the diagnosis of psoriasis. Here, we analyzed large-scale public data to identify diagnostic biomarkers of psoriasis vulgaris using two GEO databases, one list of autophagy-related genes, and four validation GEO databases. First, we identified differentially expressed autophagy-related genes (DERAGs) between non-lesional (NL) and lesional (LS) skin samples from patients with psoriasis vulgaris. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for DERAGs. We verified these genes using four GEO databases and finally acquired three autophagy-related genes, including BIRC5, NAMPT, and BCL2. In the end, our findings were validated in clinical blood samples using ELISA. Methods Microarray data and autophagy-related genes Six datasets were downloaded from GEO ([51]http://www.ncbi.nlm.nih.gov/geo/). Two [52]GPL570 datasets, [53]GSE30999^[54]12 and [55]GSE41664^[56]13, which contained 123 paired non-lesional (NL) and lesional skin (LS) samples from patients with psoriasis vulgaris, were selected as test datasets. The other two [57]GPL570 datasets, [58]GSE13355^[59]14 and [60]GSE14905^[61]15, which contained 85 normal skin (NN), 86 NL, and 91 LS samples; one [62]GPL96 [63]GSE6710 dataset^[64]16, which contained 13 paired NL and LS samples; and the [65]GPL570 [66]GSE55201 dataset^[67]17, which contained 30 blood samples from healthy controls and 44 blood samples from patients with psoriasis vulgaris, were selected as validation datasets (Table [68]1). A total of 222 autophagy-related genes (ARGs) were obtained from the Human Autophagy Database ([69]http://www.autophagy.lu/index.html). Table 1. Information for selected microarray datasets. GEO accession Platform Samples Source tissue NN NL LS [70]GSE30999 [71]GPL570 85 85 Skin [72]GSE41664 [73]GPL570 38 38 Skin [74]GSE13355 [75]GPL570 64 58 58 Skin [76]GSE14905 [77]GPL570 21 28 33 Skin [78]GSE6710 [79]GPL96 13 13(mild 5 moderate 7 severe 1) Skin [80]GSE55201 [81]GPL570 HC PS Blood 30 44 [82]Open in a new tab NN normal skin from control, NL non-lesional skin from psoriatic patient, LS lesional skin from psoriatic patient, HC healthy control, PS psoriasis patient. Identification of differentially expressed autophagy-related genes The original data downloaded from GEO using the “GEOquery” package^[83]18 were pooled and normalized by the “sva” package. The clustering of data between two groups in [84]GSE30999 and [85]GSE41664 was verified by the “umap” package. The differentially expressed genes (DEGs) of [86]GSE30999 and [87]GSE41664 were identified using the “limma” package^[88]19. The volcano plot was visualized by the “ggplot2” package. The screening criteria were |log2FC|> 1 and padj < 0.05. Venn diagrams of DEGs and ARGs were used to identify differentially expressed autophagy-related genes (DERAGs). The heatmap of DERAGs was conducted by the “ComplexHeatmap” package^[89]20. Correlation analysis of DERAGs was performed using Spearman’s rank correlation coefficient. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the “GOplot” package^[90]21. The GO and KEGG results were presented as chord and loop diagrams, respectively^[91]22. Validation of differentially expressed autophagy-related genes in four datasets The data of [92]GSE13355 and [93]GSE14905 were pooled, which contained 91 LS and 171 control samples. We first validated DERAGs in the two datasets with large samples. The [94]GSE6710 dataset contained 13 paired NL and LS samples was selected as a validation dataset due to psoriasis severity grading, in which mild was five, moderate was seven, and severe was one (Table [95]1). The [96]GSE55201 dataset was considered as a validation dataset because of blood samples, in which 7 treatment samples were omitted, 30 healthy controls and 44 psoriasis patients were retained. The data of psoriasis vulgaris patients and healthy controls A total of 17 patients with psoriasis vulgaris and 15 age-matched healthy controls were recruited from the inpatient dermatology ward of Huangshi Central Hospital between March 2022 and May 2022. The severity of disease and PASI scores were also evaluated for patients. The details of patients and controls were showed in Table [97]2. The diagnosis of psoriasis vulgaris is based on classic clinical and pathological features. All patients met the diagnostic criteria for progressive psoriasis vulgaris. None of the patients took glucocorticoids, immunosuppressants, or retinoids within 3 months, and patients were excluded if they had other types of psoriasis, such as erythrodermic psoriasis. Blood samples were collected from 17 patients with psoriasis vulgaris and 15 healthy controls. Table 2. Clinical features of cases and controls in the study. Vabriables Psoriasis vulgaris (n = 17) Control (n = 15) P value Age (years) 43.47 ± 14.14 40.27 ± 11.56 0.49 Sex (male/female) 9/8 8/7 Severity grading Mild 7 Moderate-severe 10 PASI 12.29 ± 6.16 0  < 0.0001 [98]Open in a new tab Data are presented as mean ± SD. Enzyme-linked immunosorbent assay The levels of BIRC5, NAMPT, and BCL2 in the blood samples from each participant were measured using human survivin (BIRC5) ELISA kit (ELK Biotechnology, Wuhan, China), human visfatin (NAMPT) Enzyme-linked immunosorbent assay ELISA kit (ELK Biotechnology, Wuhan, China), and human BCL2 ELISA kit (ELK Biotechnology, Wuhan, China) according to the manufacturer’s instructions. Statistics analysis Data analysis and visualization were conducted using R software (3.6.3). Correlation analysis was performed using Spearman’s correlation coefficient. When the samples satisfied the normality test, an Independent-Samples T-test was used for the two groups. When the samples did not satisfy the normality test, the Mann–Whitney U test was used for the two groups. When the samples satisfied the normality test, one-way ANOVA was used for multiple groups. If the samples did not satisfy the normality test, the Kruskal–Wallis test was used for multiple groups. The receiver operating characteristic (ROC) curve was also performed using R software (*P < 0.05; **P < 0.01; ***P < 0.001). Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Huangshi Central Hospital, Hubei, China (1.0.2022.03.31), and informed consent was obtained from all the participants. The experimental scheme was approved by the academic committee of Huangshi Central Hospital, and the experimental methods were carried out in accordance with the guidelines of the academic committee. Results Data collation and differentially expressed autophagy-related genes screening The flowchart of the study is shown in Fig. [99]1, and the collated datasets downloaded from the GEO are shown in Table [100]1. Uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification of the [101]GSE30999 and [102]GSE41664 datasets (Fig. [103]2A). The datasets [104]GSE30999 and [105]GSE41664 were selected to identify the differentially expressed genes, and a total of 1597 DEGs were identified. A volcano plot was used to visualize the DEGs, as shown in Fig. [106]2B. Venn diagrams were used to identify DERAGs, and 12 DERAGs were acquired, including 11 up-regulated genes (SERPINA1, APOL1, IKBKE, BIRC5, SESN2, EIF4EBP1, FKBP1B, NAMPT, IL24, CASP1, CCL2) and 1 down-regulated gene (BCL2) (Fig. [107]2C, Table [108]3). A complex heatmap of the 12 DERAGs is shown in Fig. [109]2D. The correlation analysis of the 12 DERAGs showed strong correlations between them (Fig. [110]3). Figure 1. [111]Figure 1 [112]Open in a new tab The flowchart of the study. DEGs, differentially expressed genes; ARGs, autophagy-related genes; DERAGs, differentially expressed autophagy-related genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes. Figure 2. [113]Figure 2 [114]Open in a new tab Screening of differentially expressed autophagy-related genes (DERAGs) in [115]GSE30999 and [116]GSE41664. (A) Uniform manifold approximation and projection (UMAP) for [117]GSE30999 and [118]GSE41664. (B) Volcano plot of 1597 DEGs including 12 DERAGs. The red and blue dots represent the up-regulated and down-regulated genes, respectively. (C) Venn diagram of the 12 DERAGs. (D) Heatmap of the 12 DERAGs in lesional skin (LS) and non-lesional (NL) samples. Table 3. The 12 differentially expressed autophagy-related genes in LS compared to NL. Gene symbol logFC Changes P value Adj. P value SERPINA1 4.226705882 Up 4.61E−50 3.49E−48 APOL1 2.683176471 Up 9.64E−52 8.46E−50 IKBKE 2.486235294 Up 1.04E−32 1.79E−31 BIRC5 2.146352941 Up 4.17E−46 2.14E−44 SESN2 1.927294118 Up 1.98E−43 7.85E−42 EIF4EBP1 1.880117647 Up 1.48E−43 5.96E−42 FKBP1B 1.760235294 Up 1.96E−19 1.21E−18 NAMPT 1.432 Up 2.82E−67 8.84E−65 IL24 1.416235294 Up 5.08E−18 2.84E−17 CASP1 1.338 Up 2.13E−55 2.47E−53 CCL2 1.305294118 Up 6.82E−27 7.25E−26 BCL2 − 1.168705882 Down 1.52E−42 5.65E−41 [119]Open in a new tab LS lesional skin from psoriatic patient, NL non-lesional skin from psoriatic patient. Figure 3. Figure 3 [120]Open in a new tab Spearman correlation analysis of the 12 DERAGs. ^*P < 0.05; ^**P < 0.01. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis The GO and KEGG pathways were analyzed for the datasets [121]GSE30999 and [122]GSE41664 using the R software GOplot package. The GO results showed the top three terms for cellular component (CC), biological process (BP), and molecular function (MF) (Table [123]S1). The top nine GO terms were selected based on a p value < 0.05 and were drawn in a chord plot (Fig. [124]4A). The KEGG pathway results indicated that the most enriched pathway was the NOD-like receptor signaling pathway (Table [125]S1). The top 10 pathways were also selected based on a p value < 0.05 and were drawn in a loop diagram (Fig. [126]4B). Figure 4. [127]Figure 4 [128]Open in a new tab GO and KEGG enrichment analyses of DEGs. (A) The chord plot showing the top 9 GO terms. (B) The loop diagram showing the 10 top pathways. Validation of 12 DERAGs in four datasets The data were analyzed using R software (3.6.3). The pooled results of [129]GSE13355 and [130]GSE14905 datasets showed that the expression levels of 11 DERAGs were increased significantly and the expression level of BCL2 was decreased significantly when compared with LS samples and NN and NL samples, which were consistent with the results of two test datasets (Fig. [131]5A). Similarly, the results of the [132]GSE6710 dataset demonstrated that the expression levels of 7 DERAGs (APOL1, BIRC5, CCL2, EIF4EBP1, IKBKE, NAMPT, and SERPINA1) were increased significantly and the expression level of BCL2 was decreased significantly when compared with LS samples and NL samples (Fig. [133]5B). In addition, we found that there were no statistical significances in their expression levels between mild and moderate-severe psoriasis samples (Fig. [134]5B). Furthermore, there was no expression of SESN2 and were no statistical significances of 3 DERAGs (CASP1, FKBP1B, and IL24) in the [135]GSE6710 dataset. Nevertheless, the results of the [136]GSE55201 dataset indicated that the expression levels of BIRC5 and NAMPT were significantly higher and the expression levels of BCL2 and IL24 were significantly lower in psoriatic blood samples than healthy blood samples (Fig. [137]5C). In summary, we validated 3 DERAGs in skin and blood samples by four different datasets. Specifically, BIRC5 and NAMPT were up-regulated, and BCL2 expression was down-regulated. Figure 5. [138]Figure 5 [139]Open in a new tab Verification of the 12 DERAGs in four datasets. (A) Verification by [140]GSE13355 and [141]GSE14905. Compared with LS samples and NN and NL skin samples, 11 DERAGs were significantly increased and BCL2 was significantly decreased. (B) Verification by [142]GSE6710. Compared with LS and NL samples, 7 DERAGs (APOL1, BIRC5, CCL2, EIF4EBP1, IKBKE, NAMPT and SERPINA1) were significantly increased and BCL2 were significantly decreased. There were no statistical significances in their expression levels between mild and moderate-severe psoriasis samples. (C) Verification by [143]GSE55201. Compared with psoriatic and healthy blood samples, BIRC5 and NAMPT were significantly increased and BCL2 and IL24 were significantly decreased. ^*P < 0.05; ^**P < 0.01; ^***P < 0.001; ns, no significant difference. ROC curves of 3 DERAGs in [144]GSE6710 and [145]GSE55201 datasets We used R software to draw ROC curves for BIRC5, NAMPT, and BCL2). The area under the curve (AUC) is an indicator of the diagnostic effect; the greater the value, the better the diagnostic effect. In the [146]GSE6710 dataset, BIRC5, NAMPT, and BCL2 had high diagnostic values in both mild and moderate-severe psoriasis vulgaris samples (Fig. [147]6A, B). All three genes had AUC values greater than 0.9, and NAMPT had the highest diagnostic value (AUC:1.000) in psoriatic skin samples (Fig. [148]6A, B). In the [149]GSE55201 dataset, BIRC5, NAMPT, and BCL2 had high diagnostic values in psoriatic blood samples and BIRC5 had the highest diagnostic value (AUC:0.786) (Fig. [150]6C). Figure 6. [151]Figure 6 [152]Open in a new tab ROC Curves of the 3 DERAGs in two datasets. (A) ROC Curves of BIRC5, NAMPT, and BCL2 in mild psoriasis skin samples. (B) ROC Curves of BIRC5, NAMPT, and BCL2 in moderate-severe psoriasis skin samples. (C) ROC Curves of BIRC5, NAMPT, and BCL2 in blood samples. Verification of 3 DERAGs in our clinical blood samples The expression levels of BIRC5, NAMPT, and BCL2 were tested by ELISA in the blood samples of patients with psoriasis vulgaris and healthy controls. The expression levels of BIRC5 and NAMPT were significantly higher, and the expression level of BCL2 was significantly lower in psoriatic blood samples than healthy blood samples (Fig. [153]7A). At the same time, no statistical significances of their expression levels existed in in blood samples of mild and moderate-severe psoriasis (Fig. [154]7A). As shown in Fig. [155]7B, the expression levels of BIRC5 and NAMPT were positively correlated with PASI scores, and the expression level of BCL2 was negatively correlated with PASI scores. ROC curves illustrated that BIRC5, NAMPT, and BCL2 had high diagnostic values in psoriatic blood samples and BIRC5 had the highest diagnostic value (AUC:0.985) (Fig. [156]7C). Figure 7. [157]Figure 7 [158]Open in a new tab Verification of the 3 DERAGs in our clinical blood samples. (A) Compared with psoriatic and healthy blood samples, BIRC5 and NAMPT was significantly increased, BCL2 was significantly decreased (P < 0.001). No statistical significances of their expression levels existed in blood samples of mild and moderate-severe psoriasis. (B) The expression levels of BIRC5 and NAMPT were positively correlated with PASI scores, and the expression level of BCL2 was negatively correlated with PASI scores. (C) ROC Curves of BIRC5 (AUC:0.985), NAMPT (AUC:0.977) and BCL2 (AUC:0.965) in clinical blood samples. ^*P < 0.05; ^**P < 0.01; ^***P < 0.001. Discussion The different types of psoriasis include psoriasis vulgaris, pustular psoriasis, erythrodermic psoriasis, and arthritic psoriasis^[159]23. Psoriasis vulgaris is the most common type, manifesting as an erythematous scaly plaque^[160]3. However, the clinical presentation of psoriasis vulgaris is sometimes atypical, resulting in frequent misdiagnosis^[161]24. Although the gold standard of diagnosis is skin biopsy, it may not be accepted by patients due to its invasiveness and the long waiting period for pathologic result. Thus, it is imperative to identify effective biomarkers for the early diagnosis of psoriasis vulgaris. In our study, we identified 1597 DEGs, including 834 upregulated and 763 downregulated genes, by comparing gene expression in 123 paired non-lesional and lesional psoriatic skin samples. GO enrichment and KEGG pathway analyses were performed. The GO enrichment analysis was more enriched in the TORC2 complex. It is worth noting that mTORC2 is an inhibitor of autophagy^[162]25. The KEGG pathway was mostly enriched in the NOD-like receptor signaling pathway. Yang et al. also revealed that KEGG pathway analysis was mainly enriched in the NOD-like receptor axis using the GEO datasets of psoriasis^[163]26. We identified 12 DERAGs using Venn diagrams. After the 12 DERAGs were validated using four GEO datasets, we acquired three DERAGs (BIRC5, NAMPT, and BCL2). In the four datasets, compared with their expression levels in psoriatic and control skin samples, BIRC5 and NAMPT were significantly upregulated, and BCL2 was significantly downregulated; compared with their expression levels in psoriatic and control blood samples, BIRC5 and NAMPT were also significantly upregulated, and BCL2 was significantly downregulated. Further, ROC curve analysis showed that the three genes have good diagnostic values for both psoriasis vulgaris skin and blood samples. To increase the reliability of the above results, we verified the expression levels of BIRC5, NAMPT and BCL2 in our clinical blood samples. These results were consistent with our expectations that BIRC5 and NAMPT were upregulated and BCL2 was downregulated in psoriatic blood samples. Additionally, there was no significant differences in their expression levels between mild and moderate-severe psoriasis patients. The three genes had good diagnostic values for clinical blood samples. Therefore, we hypothesized that BIRC5, NAMPT, and BCL2 might serve as biomarkers for the early diagnosis of psoriasis vulgaris. There are three commonly established pathways of apoptosis: extrinsic, intrinsic, and granzyme/perforin pathways^[164]27. The B cell lymphoma-2 (BCL2) family members are important components of the intrinsic pathway^[165]28. It is known to all that BCL2 is a key member of the BCL2 family, which can inhibit apoptosis and promote cell survival, and it usually has abnormal expression or function in almost all tumors^[166]29. Many studies have reported contradictory results regarding BCL2 expression in psoriatic skin. Kastelan et al. reported the upregulated expression of BCL2 in psoriatic skin^[167]30. However, Batinac et al. and Gündüz et al. showed downregulated expression of BCL2 in psoriatic skin compared with normal skin^[168]31,[169]32. The BCL2 expression in our blood samples was consistent with that in the latter. Baculoviral IAP repeat-containing 5 (BIRC5), also known as survivin, API4, or EPR-1, is an inhibitor of apoptosis^[170]33. The upregulated expression of BIRC5 can be observed in many types of cancers, such as breast cancer and esophageal cancer^[171]34,[172]35. BIRC5 may play an important role in psoriasis pathogenesis because of its effects on apoptosis^[173]36. Several studies have indicated significantly upregulated expression of BIRC5 in psoriatic skin and blood samples compared to controls^[174]36,[175]37. The BIRC5 expression in our blood samples was consistent with theirs. Nicotinamide phosphoribosyltransferase (NAMPT), also known as PBEF and visfatin, is considered an enzyme that is involved in nicotinamide adenine dinucleotide (NAD) biosynthesis^[176]38. NAMPT exists in two major forms: an intracellular form (iNAMPT) and an extracellular form (eNAMPT)^[177]38. iNAMPT plays a key role in intracellular NAD levels. eNAMPT also acts as an immunomodulatory cytokine in multiple pathways in addition to its enzymatic activity^[178]39. It has been reported that eNAMPT can be involved in various metabolic disorders and cancer^[179]40,[180]41. Several studies have revealed that the NAMPT-mediated NAD salvage pathway may contribute to the pathogenesis of psoriasis^[181]42,[182]43. Mercurio et al. reported that NAMPT expression was upregulated in psoriatic skin compared with normal skin^[183]42. The NAMPT expression in our blood samples was consistent with their results. Therefore, we considered three genes (BIRC5, NAMPT, and BCL2) as potential biomarkers for the early diagnosis of psoriasis vulgaris. However, there were some limitations in this study. First, several datasets may have resulted in unavoidable batch differences during the analysis. Second, since different datasets have different analytical methods, quite big differences maybe exist in the list of significantly changed genes. Third, the sample size of the study is rather small (17 cases and 15 controls), which weakens the strength of the results. Fourth, we also verified the three autophagy-related genes using RT-qPCR in clinical psoriasis vulgaris samples. But the mRNA expression levels of these genes in psoriatic skin samples were not statistically significant compared with those in the control groups, possibly because of insufficient psoriatic skin samples and unqualified storage conditions (n = 10). Thus, we plan to increase the sample size and improve the preservation standards in the next step for further study. Conclusion In our study, we first identified 12 differentially expressed autophagy-related genes in psoriasis vulgaris using bioinformatics analysis. We then acquired three autophagy-related genes (BIRC5, NAMPT, and BCL2). Finally, the three autophagy-related genes were successfully validated in clinical blood samples. Therefore, we hypothesized BIRC5, NAMPT, and BCL2 as potential biomarkers for the early diagnosis of psoriasis vulgaris using bioinformatics analysis and clinical samples. Moreover, the three autophagy-related genes may influence the pathogenesis of psoriasis by regulating autophagy. These results may provide clues for the development of new targeted therapies. Supplementary Information [184]Supplementary Information.^ (16.9KB, docx) Author contributions A.L.Z. and B.Z. wrote the main manuscript text and Y.J.C, T.S.L. and T.Y. prepared figures 1–7. All authors reviewed the manuscript. Funding This work was supported by the Nature Science Foundation of Hubei Province (2023AFD021) and the Scientific Research Project of Hubei Polytechnic University (23xjz03Y). Data availability The gene expression profiles of [185]GSE30999, [186]GSE41664, [187]GSE13355, [188]GSE14905, [189]GSE6710 and [190]GSE55201 were downloaded from Gene Expression Omnibus GEO) ([191]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30999, [192]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41664, [193]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13355, [194]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14905, [195]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6710, and [196]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55201). Competing interests The authors declare no competing interests. Footnotes Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-023-49764-0. References