Major depressive disorder (MDD) is a prevalent mental illness characterized by symptoms such as a depressed mood, diminished interest or pleasure in activities, reduced energy, and suicidal ideation (1). The incidence of depression is on the rise, with an estimated 300 million individuals globally affected by MDD (2). Projections indicate that depression may become the leading cause of the global burden of disease by 2030 (3). MDD is associated with a high disability rate, significant suicide risk, substantial societal burden, a tendency for recurrence, and a challenge in timely access to effective treatment for the majority of patients (4). These factors have positioned MDD as a significant public health and social issue, adversely impacting the physical and mental well-being of individuals. In contrast to the substantial patient population and disease burden, the current diagnostic landscape for mental illnesses is limited by the absence of effective, straightforward, and reliable objective diagnostic methods. This deficiency impedes the accurate diagnosis, treatment, and pathophysiological research of mental disorders. Consequently, the identification of diagnostic biomarkers has emerged as an urgent and critical priority, essential for facilitating precise diagnosis and treatment strategies.

Despite the large number of experimental and clinical studies conducted to elucidate the pathogenesis of depression, including genetic, biological, psychological, and social determinants, our understanding of the etiology of MDD remains incomplete (5, 6). Antidepressant medications available, 30-50% of patients with MDD do not achieve complete remission, suggesting that conventional treatments do not address important biological processes involved in MDD pathology (7). The genetic factors in the pathogenesis of MDD are complex and involve the interaction of multiple genes with environmental factors. Research has shown that MDD is polygenic, meaning that it is affected by the combined effects of multiple genes rather than a single gene. Rapid advances in sequencing technology and bioinformatics have made it possible to explore the disease pathogenesis at the gene level (8). One of the more important methods is weighted gene co-expression network analysis (WGCNA). This analysis method aims to find co-expressed gene modules and explore the association between the gene network and the phenotype of interest, as well as the core genes in the network (9). In this study, using abundant public resources and bioinformatics methods, the hub genes associated with depression were identified through differential analysis of the GEO database and WGCNA, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to further investigate the biological processes and pathways. The functional enrichment pathway analysis revealed that these differential expressed genes (DEGs) were associated with immunity.

Evidence from numerous studies indicates that MDD is associated with immune dysregulation, including monocyte activation, reduced T-cell numbers and activity, and elevated pro-inflammatory cytokine production (10, 11). To understand the relationship between MDD and the immune system, we conducted a comprehensive analysis of DEGs in MDD, genes from WGCNA module, and immune-related genes using a Venn diagram approach. This analysis revealed 21 overlapping genes, which were further subjected to protein-protein interaction (PPI) network analysis and ClueGo analysis. Furthermore, the least absolute shrinkage and selection operator (Lasso) was used to further identify the key genes. Ultimately, we identified four hub genes, which were validated on three datasets, respectively, and an additional external verification set, GSE39653, was used for validation. The area under the curve (AUC) values indicated the superior diagnostic performance of the model, offering prospects for the diagnosis of MDD. Additionally, we investigated immune infiltration patterns and the correlation of various immune factors with the identified hub genes. The expression levels of these hub genes were examined in peripheral blood mononuclear cells (PBMCs), revealing significant differences. Finally, pathway analysis of the four hub genes showed that TLR2 and IL7R were more closely involved in the immune pathway, therefore, we conducted network pharmacological analysis and molecular docking specifically on these two genes. In conclusion, given that the pathogenesis of MDD remains ambiguous and an effective biomarker is lacking in clinical practice for the identification and accurate treatment of MDD. In this study, immune-based hub genes and drug targets were identified through diverse bioinformatics approaches, which are anticipated to guide the diagnosis and enable accurate treatment in MDD patients. This study enriches our understanding of the pathogenesis of psychiatric disorders and provides a systematic framework for comprehending the molecular basis of MDD, offering novel insights into its diagnosis and treatment. The workflow of this study is presented in Figure 1 .

MDD is a highly heterogeneous disease characterized by high morbidity and mortality and is considered the most prevalent cause of disability worldwide (1). However, the pathophysiologic mechanisms of MDD are unknown, and the biomarkers used for diagnosis are unclear. In this study, we first scrutinized the RNA expression data of MDD patients and HCs to screen for DEGs. GO, KEGG enrichment analyses showed that DEGs were mainly concentrated in immune-related pathways. To explore the profound influence of immunity on the pathogenesis of MDD more deeply, we combined multiple bioinformatics approaches to identify four hub genes (TLR2, RETN, HP, and IL7R) as key biomarkers of MDD, they showed significant differential expression between MDD patients and HCs. In addition, we used HIT to screen the herbal compounds of TLR2 and IL7R, and performed molecular docking with their respective herbal compounds to identify three herbal ingredients, Apicidin, Corilagin and Baicalin, which may be used as antidepressants in the future to treat MDD patients. Importantly, the identification of such biomarkers will enhance preventive interventions and provide valuable insights into patients’ responses to antidepressant treatments. This advancement will further facilitate the monitoring of treatment effectiveness in patients. Consequently, the development of specific depression-related biomarkers is essential for diagnosing and tracking individuals with depression, potentially leading to earlier diagnosis and timely intervention, thereby significantly improving patient outcomes (20).

GO enrichment analysis showed that the DEGs screened from the merged dataset were mainly related to humoral immune response, specific granule, and tertiary granule, and immune receptor activity. A large genome-wide transcription study in the United Kingdom found that genes aberrantly expressed in MDD are involved in the regulation and implementation of the innate immune response (21). Another study suggests that humoral immune response may be a key link between brain and blood in MDD (22). A study demonstrated a clear association between the CD300f immune receptor and MDD in females, with CD300f-/- mice exhibiting several characteristic MDD features, such as an increased number of microglia and increased interleukin 6 and interleukin 1 receptor antagonist messenger RNA, as well as persistent depressive-like and pleasure-deprived behaviors (23). According to KEGG pathways analysis, DEGs were abundantly enriched in the IgA production, hematopoietic cell lineage, and inflammatory bowel disease. As early as 2012 Maes et al. demonstrated that patients with MDD are accompanied by abnormalities in the microbiota-gut-brain axis, as evidenced by elevated IgM/IgA of some gram-negative bacterial lipopolysaccharides (LPS) in serum (24). Studies have shown that many immune disorders have a high chance of co-morbidities with MDD, such as inflammatory bowel disease, rheumatoid arthritis, etc (25). The results also show that DEGs are enriched in immune response regulation signal pathway. A previous study analyzed the whole blood of MDD patients with RNA-Seq and pathway, and the results showed that the immune system-related pathways of MDD patients were up-regulated, such as Interferon alpha/beta signaling and cytokine signaling (26). Another study showed that a variety of complement pathway related proteins may regulate the immune pathogenesis of major depression (27).

CluGO analysis of the 21 shared genes showed that 70% of the genes were focused on positive cellular regulation in response to macrophage colony-stimulating factor stimulation. Macrophage colony-stimulating factor (M-CSF) has been referred to as a “secret weapon” (28) and “master regulator” (29) because of its central role in regulating various immune cells, linking innate and adaptive immunity. A study of 16 cytokines and growth factors in patients with MDD showed that M-CSF was elevated at both baseline and in response to stress (30). However, another study showed no change in M-CSF in patients with MDD (31). 20% of these genes were associated with peptide antigen assembly with MHC class II protein complex. Major histocompatibility complex (MHC) Class II molecules are primarily involved in antigen presentation. During tissue or organ transplantation, class II molecules are important target antigens that cause transplant rejection, including host-versus-graft and graft-versus-host responses. In the immune response, class II antigens are mainly responsible for coordinating the interactions between immune cells and regulating the humoral and cellular immune responses. The findings of a study showing reduced expression of MHC II-related genes in the brain in MDD also support the long-held hypothesis that altered immune function is associated with the pathophysiology of psychiatric disorders (32).

Several previous studies have also reported some characteristics of diagnostic biomarkers for MDD. For example, the authors of the original data reported that a total of 165 adaptive immunity-related genes were differentially expressed in the dataset GSE98793, and they found that the area of the higher AUC curve in the combination consisting of 165 genes was 0.71 (33). In addition, Chan et al. identified an optimal panel of 33 immunoneuroendocrine biomarkers with moderate to good performance in distinguishing depressed patients from controls (0.69 < AUC < 0.86) (34). Furthermore, Papakostas et al. reported high diagnostic performance of serum levels of nine biomarkers (alpha1 antitrypsin, apolipoprotein CIII, brain-derived neurotrophic factor, cortisol, epidermal growth factor, myeloperoxidase, prolactin, resistin and soluble tumor necrosis factor alpha receptor type II) in independent samples from patients with MDD, with sensitivity and specificity of > 80% (35). In contrast, we found in the present study that there were 4 hub genes with an AUC of 0.92 in the GSE76826 dataset. Our observations suggest that the combination of biomarkers showed good diagnostic performance.

MDD is a complex polygenic disease. In our study, RT-qPCR analysis showed that compared with HCs, the expression of IL7R in MDD patients was significantly down-regulated (p < 0.05), while the expression of TLR2, RETN and HP was significantly up-regulated. Of these four genes, TLR2, RETN and HP, which have been previously reported to have a clear correlation with MDD. Toll-like receptors (TLRs) play a central role in innate immunity by recognizing pathogens and damage-associated molecular patterns and are associated with a wide range of inflammatory and autoimmune disorders (36). TLR profiles predict response to antidepressant treatments. Elevated levels of TLR2 may lead to suicidality in patients with MDD (37), and depression patients experience a decrease in TLR2 levels after treatment (38). Our KEGG analysis also showed that immunity and inflammation are important pathways in MDD. Therefore, TLR2 may be a key mechanism leading to MDD and immunity. The GSEA results showed that TLR2 was associated with acute myeloid leukemia (AML), and the study demonstrated a significant association between TLR2 polymorphisms and the risk of serious infections in AML patients (39). Resistin (RETN) is a peptide secreted by adipocytes that plays a role in metabolism (40). A recent meta-analysis showed that individuals with MDD have lower serum levels of resistin compared to healthy individuals (41). RETN was found to be associated with free cortisol concentrations and treatment outcomes in patients with MDD (42). In addition, RETN is involved in the interrelationship between MDD and diabetes (43). GSEA results showed that RETN was related to the biological process of RNA degradation.

Haptoglobin (HP) is a plasma protein that binds to free hemoglobin and prevents hemoglobin-driven oxidative stress. HP plays an important role in regulating the immune response (44). An increasing number of studies have reported elevated levels of methemoglobin in patients with MDD, and methemoglobin levels are significantly correlated with the number of circulating immune cells (e.g., leukocytes, monocytes, and neutrophils) (45, 46). Notably, another variant of HP is the protein called “zonulin,” which is a tight junction protein that regulates the intestinal and blood-brain barriers (47). Zonulin has also been reported to be overexpressed in the plasma of patients with MDD, suggesting that the microbiota-gut-brain axis may underlie the pathogenesis of MDD. In our study, the results of GSEA showed that the gene was related to FC gamma r mediated phagocytosis, which may be involved in the occurrence of diseases through this pathway. IL-7, also known as lymphopoietin, belongs to the IL-2/IL-15 family of cytokines (48). IL-7 binds to IL-7R, which consists of a high-affinity α subunit (CD127) and a common γ chain (49). IL-7R is physiologically expressed on CD4 and CD8 T cells and myeloid cells, but not on human B cells (50). IL-7 is thought to support aberrant immune activity in autoimmune diseases such as diabetes mellitus and multiple sclerosis (51). In our study, IL-7R was shown to be highly correlated with immune cell T cells CD8, consistent with the above study. However, the relationship between IL7R and MDD has not been reported. IL7R was decreased in MDD patients and enrichment analyses showed that the gene was involved in cellular functions, such as being involved in the regulation of the activity of RNA polymerase transcription factors.

In addition, the analysis of hub genes’ pathways by GO and KEGG shows that it mainly focuses on PI3K-Akt signaling pathway, Toll-like receptor signaling pathway and JAK-STAT signaling pathway. Many pathways are related to immune system or signal transduction. PI3K-Akt pathway is an intracellular signal transduction pathway, which is involved in many biological processes, including metabolism, proliferation, cell survival, growth and angiogenesis (52). PI3K-Akt pathway also contributes to immune regulation, and participates in chemotaxis and phagocytosis of neutrophils, activation of B cell receptor signal transduction and maturation of dendritic cells (53). Previous studies have shown that Toll-like receptor signaling pathway induces the production of pro-inflammatory cytokines by activating NF-κB and mitogen-activated protein kinase (MAPK), which leads to the rapid activation of innate immunity. This pathway is also related to PI3K-Akt signaling pathway and JAK-STAT signaling pathway, apoptosis, complement and other processes (54). Our study identified more immune signaling pathways related to MDD, which is helpful to better study the immune mechanism of MDD development.

Over the past few decades, a growing body of evidence has emphasized the role of immune mechanisms in the development of MDD. A variety of immune cells have been implicated in the development of depression (55). Therefore, it is important to assess the relationship between MDD and immune cells. By using the CIBERSORT algorithm, our analysis identified multiple immune cell subtypes in MDD cases, further revealed significant differences in immunity between MDD and normal samples. In particular, the proportions of NK cells resting, Neutrophils, and Macrophages M0 were higher in MDD patients than in healthy controls, whereas the proportions of T cells CD8 and Mast cell resting were relatively low. Our findings are consistent with a recently published study showing that depressed patients have over-expression of innate immunity genes and under-expression of genes involved in adaptive immunity (33). We further correlated the four genes with these five immune cells and showed that TLR2 had strong correlations with NK cells resting.

Although the pharmaceutical industry has invested heavily, the success rate of new drug development continues to decline (56). Therefore, it is necessary to set goals with higher probability of success as soon as possible. Herbs with antidepressant effects, including prescription drugs, individual herbs and phytochemicals, are also widely used to treat depression. In this study, we used HIT to screen the herb components of TLR2 and IL7R, and to make molecular docking. Apicidin is a histone deacetylase inhibitor. A previous study showed that Apicidin treatment has the potential to reverse learning and memory disorders in mice with Alzheimer’s disease (57). However, there are few studies on Apicidin and MDD, and MDD and Alzheimer’s disease have many common pathogeneses. Whether we can talk about the use of Apicidin in the treatment of MDD in the future needs further study. Various studies have shown that Baicalin can improve the depression-like behavior in animal models by regulating HPA axis, promoting neurogenesis, improving mitochondrial dysfunction, inhibiting neuronal inflammation and oxidative stress, and weakening neuronal apoptosis (58). These flavonoids exist in the roots of Scutellaria baicalensis Georgi, a traditional Chinese medicine. Scutellaria baicalensis Georgi is one of the components of Xiaochaihu Decoction, which is widely used in clinical depression in China. Corilagin is a polyphenol monomer isolated from Phyllanthus urinaria, which has many pharmacological properties, including anti-tumor, anti-oxidation and anti-inflammatory effects. Previous studies have reported that Corilagin can alleviate inflammatory reaction by inhibiting TLR3 signaling pathway and reducing the release of inflammatory cytokines (59). However, the relationship between Corilagin and MDD has not been clarified, which can be used as our future research direction.

One limitation of our analysis is that it only shows that the identified drugs may be useful for the mental illness under consideration, but we are not sure which specific symptoms these drugs can solve. Further research is still needed to deepen our understanding of the safety, contraindications, interactions with other drugs and the fine molecular mechanism of potential beneficial effects of these extracts. In particular, this herbal plant may also act through unexplored biological pathways, which are related to various mental disorders.

Our study also has some limitations. Compared with previous WGCNA analyses of other diseases, our sample size was insufficient and there may be some bias. We have identified many new hub genes and enrichment pathways, however, the molecular mechanism of how it plays its role remains unclear. In this study, we merely analyzed the mRNA expression levels of hub genes in MDD patients and HCs, and did not undertake further exploration of the relationship between the clinical severity of patients and gene expression levels. This aspect requires further investigation in the future to enhance our understanding of the pathological mechanism of depression. In addition, the hub genes of MDD have been found to be associated with different immune factors, suggesting that they may also play important roles in the immune microenvironment. However, their specific roles remain to be further investigated.

In conclusion, we identified for the first time four hub genes (TLR2, RETN, HP, and IL7R), which may be diagnostic biomarkers for MDD. In addition, we constructed an immune-related diagnostic model for MDD using Lasso regression analysis, which showed good diagnostic performance in the dataset GSE98793. Thus, our genetic characterization can provide an accurate and reliable prediction method for patients. Finally, the immune cell infiltration of MDD patients was analyzed using CIBERSORT, and correlation analysis showed that hub genes are involved in the immune response in MDD. Our study combined with further validation of blood samples from clinical patients provides strong evidence for studies exploring immune involvement in the pathophysiologic pathogenesis of MDD, which will be a new starting point. In the future, to validate the results of our analysis, it is essential to expand the sample size and conduct multi-center, large-sample randomized controlled trials alongside biological investigations. These efforts will help elucidate how hub genes and hub pathways influence the development of affective disorders. We recommend identifying subgroups of mental diseases that exhibit different phenotypes yet share common pathogenic mechanisms. This approach aims to facilitate the development of new drugs targeting shared molecular biological pathways. Furthermore, drug development should prioritize targeting these biological pathways rather than focusing solely on isolated genes or proteins.