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Article

Identification of Common Hub Genes in COVID-19 and Comorbidities: Insights into Shared Molecular Pathways and Disease Severity

by
Suresh Kumar
1,*,
Jia-Jin Wee
1 and
K. J. Senthil Kumar
2,3,*
1
Faculty of Health and Life Sciences, Management and Science University, Shah Alam 40100, Malaysia
2
Department of Biotechnology, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105, Tamil Nadu, India
3
Center for General Education, National Chung Hsing University, Taichung 402, Taiwan
*
Authors to whom correspondence should be addressed.
COVID 2025, 5(7), 105; https://doi.org/10.3390/covid5070105
Submission received: 10 May 2025 / Revised: 30 June 2025 / Accepted: 1 July 2025 / Published: 8 July 2025
(This article belongs to the Section Host Genetics and Susceptibility/Resistance)
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Abstract

Severe COVID-19 disproportionately impacts patients with comorbidities such as type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD), affecting 10–30% of cases. This study elucidates shared molecular mechanisms by identifying common hub genes and genetic variants across these conditions using an integrative bioinformatics approach. We curated 5463 COVID-19-related genes from DisGeNET, GeneCards, T-HOD, and other databases, comparing them with gene sets for T1D (324 genes), T2D (497), OBCD (835), CVD (1756), HTN (837), and CeVD (1421). Functional similarity analysis via ToppGene, hub gene prediction with cytoHubba, and Cytoscape-based protein–protein interaction networks identified four hub genes—CCL2, IL6, IL10, and TLR4—consistently shared across all conditions (p < 1.0 × 10−5). Enrichr-based gene ontology and KEGG analyses revealed cytokine signaling and inflammation as key drivers of COVID-19 cytokine storms. Polymorphisms like IL6 rs1800795 and TLR4 rs4986790 contribute to immune dysregulation, consistent with previous genomic studies. These genes suggest therapeutic targets, such as tocilizumab for IL6-driven inflammation. While computational, requiring biochemical validation, this study illuminates shared pathways, advancing prospects for precision medicine and multi-omics research in high-risk COVID-19 populations.

1. Introduction

On 21 December 2019, the virus responsible for COVID-19, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in China. Since then, the virus has spread to more than 180 countries and continues to evolve, with emerging variants and the persistence of long COVID contributing to ongoing public health challenges. As of early 2025, over 900 million confirmed infections have been reported worldwide, with official death counts of approximately 7 million—although modeling studies suggest that the true global toll may exceed 20 million. They were identified to be a zoonotic virus, which are viruses that are transferred from a non-human animal to a human. Studies show that the bats were the likely reservoirs, due to it being genetically similar to the SARS bat viruses [1]. The single-stranded positive-sense RNA was encapsulated by its nucleocapsid, with it surrounded by spike proteins, which help with receptor recognition and the cell membrane fusion process [2]. Computational studies of SARS-CoV-2 variants, such as Delta and Omicron, have further elucidated differences in spike protein structure and function, potentially contributing to variations in infectivity and immune evasion [3]. Clinically, infection typically presents with respiratory symptoms such as dry cough, sore throat, difficulty breathing, and fever, which generally appear within two or more days after exposure [4].
Despite the unprecedented global vaccination campaign—with more than 14 billion doses administered worldwide, including periodic booster shots updated to counter new mutations—and advances in therapeutic approaches such as drug repurposing [5] and vaccine booster development [6], COVID-19 remains a significant health crisis. For instance, computational assessments of Omicron sub-variants (BA.1, BA.1.1, BA.2, and BA.3) have highlighted increased spike protein infectivity, which may exacerbate disease outcomes in patients with comorbidities [7]. Additionally, long COVID, characterized by persistent symptoms, shares molecular pathways with chronic comorbidities, as revealed by computational analyses of SARS-CoV-2 gene expression, highlighting the need for targeted therapies in high-risk groups [8].
Comorbidities refer to one or more conditions that are present along with a main disease. These conditions are typically chronic, which can affect an individual’s ability to function. This also means that comorbidities can affect the individual in terms of being more vulnerable to getting a disease. COVID-19 is no exception, as there have been reports of comorbidities present in people with the disease [4,9,10,11]. Among the most prevalent comorbidities, type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD) affect 10–30% of COVID-19 patients, with T2D (20–30%) and CVD (10–15%) being particularly common [3,6,7,8]. These conditions elevate severe outcome risks, with odds ratios of 1.5–3.0 for mortality and 30–50% ICU admission rates for T2D patients [6,7]. These comorbidities were selected due to their high prevalence, strong association with severe COVID-19, and robust gene set availability in databases like DisGeNET, T-HOD, and GeneCards, unlike chronic obstructive pulmonary disease (COPD) or chronic kidney disease (CKD), which lack sufficient curated genetic data [3,6]. The World Health Organization emphasizes that all age groups are susceptible, but the elderly with these comorbidities face the highest risk due to age-related immune and physiological declines [3].
Genomic and biochemical studies have identified several susceptibility genes associated with COVID-19 severity and immune response. For instance, genome-wide association studies (GWAS) have pinpointed variants in genes such as ACE2, TMPRSS2, and HLA loci that influence SARS-CoV-2 entry and immune regulation [12,13]. Additionally, genes involved in interferon signaling, such as IFITM3 and IRF7, have been linked to differential disease outcomes [14]. Studies have also highlighted the role of inflammatory and immune response genes, including CCL2, IL6, IL10, and TLR4, in modulating COVID-19 severity [15,16]. These findings underscore the importance of genetic factors in determining susceptibility and disease progression, providing a foundation for our bioinformatics analysis of shared hub genes between COVID-19 and its comorbidities.
While prior studies have identified susceptibility genes for COVID-19. There are currently not enough studies explaining why and how the common comorbidities are linked to COVID-19 in terms of genetics. With how prevalent COVID-19 is in people with comorbidities, there could be potential answers that lie in genetics that may potentially play a role. Shared genetic factors likely contribute to the increased severity of COVID-19 in patients with comorbidities, potentially through common molecular pathways that influence disease susceptibility and progression. Knowledge of gene similarities among COVID-19 and the comorbidities can prove to be useful for future prospects, such as in drug design, where it can inhibit the actions of these particular genes common among all the comorbidities. Despite advances in identifying COVID-19 susceptibility genes, the genetic links between COVID-19 and its common comorbidities remain underexplored. Shared genetic factors likely underlie the heightened severity in patients with T1D, T2D, OBCD, CVD, HTN, and CeVD, potentially through pathways like cytokine signaling and inflammation that exacerbate SARS-CoV-2 pathogenesis [9]. Understanding these shared mechanisms could inform targeted therapies, such as drugs inhibiting pro-inflammatory genes, to mitigate severe outcomes in high-risk populations [14]. This study employs an integrative bioinformatics approach, leveraging databases (DisGeNET, T-HOD, GeneCards, CTD) and tools (ToppGene, Cytoscape 3.8.0, cytoHubba 0.1, ToppGene Suite, Cincinnati, OH, USA) to identify common hub genes and genetic variants across COVID-19 and these comorbidities. By predicting shared hub genes via interactome analysis, we aim to uncover novel molecular insights, offering potential biomarkers and therapeutic targets to address the compounded effects of COVID-19 in patients with chronic conditions.

2. Materials and Methods

2.1. Gene Set Retrieval

Gene sets related to COVID-19 were obtained from various online databases, including the Comparative Toxicogenomics Database (CTD) [17], the Gordon study [18], and DisGeNet [19]. Additionally, a gene expression list specific to the SARS-CoV-2 virus was created using data from the SARS-CoV-2 Infection Database [20] and the H2V [21], which includes human proteins/genes responding to SARS-CoV-2. Only unique genes from these databases were collected and merged for the investigation. Gene sets for diabetes (both type 1 and type 2), obesity, and hypertension were sourced from the Text-mined Hypertension, Obesity, and Diabetes (T-HOD) database [22]. T-HOD identifies genes related to these cardiovascular diseases through advanced text-mining technologies that extract relevant genes from newly published literature, helping to affirm gene-disease associations. The cardiovascular disease gene set was retrieved from DisGeNet, a comprehensive database that integrates data from multiple repositories and literature sources (https://www.disgenet.org/). The cerebrovascular disease gene set was obtained from GeneCards [23], which provides extensive genomic information on both predicted and known human genes. Gene sets for COVID-19 were retrieved from DisGeNET, CTD, the Gordon study, and the SARS-CoV-2 Infection Database between January and March 2024. In DisGeNET, we used search terms “COVID-19” and “SARS-CoV-2,” filtering for human genes with a gene-disease association score >0.1. In CTD, we applied filters for “Homo sapiens” and curated interactions. The SARS-CoV-2 Infection Database was queried for genes differentially expressed in infected human cell lines, with a p-value cutoff of <0.05. A total of 5463 unique COVID-19-related genes were obtained after merging gene lists and removing duplicates using Ensembl gene IDs. For comorbidities, gene sets were retrieved as follows: T1D (324 genes), T2D (497 genes), OBCD (835 genes), CVD (1756 genes), HTN (837 genes), and CeVD (1421 genes) were sourced from T-HOD, DisGeNET, and GeneCards, with similar search and filtering strategies applied. Search terms included “type 1 diabetes”, “type 2 diabetes”, “obesity”, “cardiovascular disease”, “hypertension”, and “cerebrovascular disease”, with filters for human genes and literature-based evidence. Based on previous studies of clinical data on COVID-19, comorbidities like cardiovascular disease (CVD), including hypertension and diabetes, have been the most prevalent [4,24]. These comorbidities were selected because they are among the most prevalent and strongly associated with severe outcomes in COVID-19 patients. They represent major risk factors that influence the progression of COVID-19.
Mining from diverse gene databases is crucial for this study, as it ensures a comprehensive and well-rounded collection of gene sets. Different databases offer unique datasets and methodologies for gene curation, which helps in capturing a broader spectrum of relevant genes. For example, literature mining in T-HOD allows for the inclusion of recent research findings, while databases like DisGeNet and GeneCards provide integrated information from various sources and repositories. By utilizing multiple databases, we can enhance the robustness of our gene sets, minimize the risk of missing key genes, and improve the accuracy of our analysis. This comprehensive approach ensures that the gene sets used are representative of the current understanding of each condition, ultimately providing more reliable insights into the common hub genes and their potential roles in COVID-19 and its comorbidities.

2.2. Candidate Gene Prioritization Using ToppGene

We employed ToppGene [25] (http://toppgene.cchmc.org), a specialized bioinformatics tool for gene prioritization. ToppGene employs functional annotation-based candidate gene prioritization using a fuzzy-based similarity measure to assess the similarity between genes based on their semantic annotations. The similarity scores from individual features are combined into an overall score through statistical meta-analysis. A p-value for each annotation of a test gene is determined by random sampling from the entire genome. The method also incorporates a protein–protein interaction network (PPIN) to further enhance disease gene prioritization. This approach systematically ranked candidate genes based on their functional similarity to a curated list of genes associated with COVID-19, using ToppGene’s fuzzy-based similarity measure. The objective was to identify genes that might share functional relevance with the viral infection, potentially providing new insights into the connection between COVID-19 and other comorbidities.
For this analysis, we used a training set composed of a carefully curated list of genes implicated in COVID-19. These genes are known to be involved in various biological processes and pathways related to the infection and its associated complications. The test sets consisted of candidate genes associated with six distinct comorbid diseases: type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD). The analysis utilized various training parameters, including Gene Ontology categories (Molecular Function, Cellular Component, Biological Process), as well as Human and Mouse Phenotypes, Pathways, PubMed references, Interactions, and Co-expression data. Additionally, specific datasets from the ToppCell atlas were selected, focusing on COVID-19-related data such as bronchoalveolar lavage (BAL) samples, autopsy data, and immune cell atlases from both peripheral blood mononuclear cells (PBMCs) and cerebrospinal fluid. The study integrated multiple COVID-19 datasets, including those from patients with other immune-mediated diseases like influenza and sepsis, and included data from SARS-CoV-2-infected human cell lines. These candidate genes were selected based on their identification in existing literature and public databases as potentially linked to these conditions.
ToppGene was then utilized to perform a functional similarity analysis between the COVID-19 training set and each of the disease-specific comorbidity test sets. We first extracted the functional characteristics of the genes in the COVID-19 training set, including gene ontology (GO) terms, biological pathways, transcription factor binding sites, protein–protein interactions, and other relevant annotations. The functional features of each gene in the test sets were then compared against those of the COVID-19 genes, with ToppGene assessing the degree of similarity between the functional profiles of the training set and each candidate gene in the test sets. Based on this analysis, ToppGene prioritized and ranked the candidate genes within each test set according to their functional similarity to the COVID-19 genes, with genes exhibiting the highest similarity scores being ranked highest. This indicated a stronger potential for involvement in both COVID-19 and the respective disease.
Each test set, including T1D, T2D, OBCD, CVD, HTN, and CeVD, was analyzed separately against the COVID-19 training set to ensure that the prioritization captured disease-specific insights. The results from these comparisons highlighted genes that are most likely involved in both COVID-19 and the individual diseases, offering potential new targets for therapeutic intervention or biomarker discovery. This methodology provided a robust framework for identifying candidate genes that could serve as potential therapeutic targets or biomarkers, thereby bridging the gap between COVID-19 and comorbid diseases.

2.3. Network Analysis Using Cytoscape

The common genes of diabetes and COVID-19 obtained in the previous step were analyzed using Cytoscape. Using the STRING protein search function, the common genes of COVID-19 and diabetes type 1, obtained from the previous step, were input into the search bar, and the network was generated. Then, the organic layout at the layout menu was selected, and nodes were adjusted to look like a circular layout, and the figure was saved. This step was repeated for other common genes of other comorbidities like T2D, OBCD, CVD, HTN, and CeVD.

2.4. Prediction of Hub Genes Using CytoHubba

CytoHubba is a Cytoscape plugin designed specifically to identify hub genes in complex biological networks. It allows users to apply various topological algorithms to rank and identify the most important nodes (genes) within a network. The maximal clique centrality (MCC) method, used in this study, is particularly effective at identifying highly interconnected hub genes. MCC is often preferred because it is robust and can identify nodes that are part of dense clusters, which might be biologically significant. Hub genes were predicted using cytoHubba. After completing the previous calculations, the top 20 hub genes for COVID-19 and type 1 diabetes were identified using the MCC method. A network of these 20 hub genes was then generated and organized using the circular layout. This process was repeated for the common genes shared between COVID-19 and its comorbidities like T2D, OBCD, CVD, HTN, and CeVD.

2.5. Identification of Common Hub Genes

To identify common hub genes that might play a pivotal role in both COVID-19 and its comorbidities, we conducted a comparative analysis using Venn diagram tools. This analysis is crucial because overlapping hub genes between COVID-19 and comorbidities such as type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD) could reveal shared molecular mechanisms. Understanding these shared mechanisms is essential for uncovering potential therapeutic targets that address both the viral infection and its associated chronic conditions. For this purpose, Venny 2.1.0 was initially used to identify common genes among the top 20 hub genes from each disease. To handle comparisons involving more than five lists, we utilized Interactivenn, a tool designed to efficiently compare multiple gene lists [26]. The significance of gene overlaps was assessed using hypergeometric tests, with p-values calculated to determine the likelihood of observing the overlap by chance, based on the total number of genes in the human genome (~20,000). To mitigate database bias, gene sets were retrieved from multiple sources (DisGeNET, T-HOD, GeneCards, CTD) to ensure comprehensive coverage, and duplicates were removed using Ensembl gene IDs to prevent overrepresentation of frequently reported genes. This approach minimized biases inherent to individual databases, such as literature-based curation in T-HOD or experimental data emphasis in CTD. The analysis involved examining the top 20 hub genes from each condition and identifying those genes that were common across multiple conditions. Venn diagrams proved to be a useful tool in this context, as they visually highlighted the overlap between gene sets, making it easier to identify and focus on the genes that might contribute to the intersection of COVID-19 and related diseases. Following the identification of common hub genes, we visualized the protein–protein interaction network of these genes and constructed a diseasome network encompassing all the hub genes using Cytoscape. This visualization helped to illustrate the relationships and interactions between the genes, further emphasizing the significance of those that were common across conditions. The common genes identified in this analysis were recorded as potential key players in both COVID-19 and its comorbidities, providing valuable insights.

2.6. Gene Ontology and KEGG Pathway Analysis

For gene enrichment analysis, we selected Enrichr due to its comprehensive capabilities in analyzing gene sets through Gene Ontology (GO) and KEGG pathway analysis. Enrichr is a powerful tool known for its extensive database integration and user-friendly interface, making it highly suitable for identifying functional categories and pathways associated with gene sets. To perform the analysis, we input the recorded hub genes into Enrichr. The tool was used to analyze these genes through various ontologies and pathways. In the Ontologies tab, we selected GO Biological Process, GO Molecular Function, and GO Cellular Component. This selection ensured that the analysis reflected the most current and relevant annotations available at that time. Additionally, in the pathways we chose KEGG Human to analyze the pathways related to the hub genes. Studying common hub genes for gene enrichment is particularly important because these genes are likely to play significant roles in the biological processes and pathways shared between COVID-19 and its comorbidities. By focusing on these common genes, we can gain deeper insights into the underlying mechanisms and identify shared functional and pathway associations. This approach helps in understanding the broader implications of these genes across different conditions and may uncover potential therapeutic targets that address multiple diseases simultaneously.

3. Results and Discussion

The total number of COVID-19 genes retrieved for this study is 5463 genes. As for the comorbidities, 324 genes were obtained for T1D, 497 genes for T2D, 835 genes in the case of OBCD, 1756 genes retrieved for CVD, 837 genes obtained for HTN, and finally 1421 genes for CeVD. The networks of common genes among COVID-19 and comorbidities were created to illustrate their interactions, making a total of 6 common gene networks (Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5). In all networks, green nodes represent the genes associated with COVID-19 and their respective comorbidity, while red nodes indicate that the genes are genes of high association calculated by the MCC method of cytoHubba.
Figure 1 illustrates the protein–protein interaction (PPI) network for the common genes identified in both COVID-19 and type 1 diabetes (T1D) and type 2 diabetes (T2D). The network was generated using interactome analysis, revealing key interactions. Each node represents a protein, while the edges indicate direct or indirect interactions between them. The central hub proteins, CCL2, IL6, IL10, and TLR4, are highlighted, demonstrating their prominent roles in both the immune response and the cytokine signaling pathways associated with COVID-19 and diabetes. The figure also shows how these proteins interact with other immune-regulatory proteins, suggesting their involvement in the cytokine storm during COVID-19 infection, as well as their role in autoimmune dysregulation. The thickness of the edges represents the strength of the interaction, with thicker lines indicating stronger or more frequent interactions, which may point to the higher importance of those interactions in the pathogenesis of both conditions. The top 20 hub genes calculated by the MCC method of cytoHubba were also visualized in a network for T1D and T2D, as shown in Figure 1. A color gradient of red to yellow was used to visualize the ranking of the nodes. Red represents genes that were ranked highest, while yellow are the genes ranked lowest, and orange nodes are genes ranked in between.
Figure 2 presents the PPI networks for common genes between COVID-19 and (a) hypertension (HTN) and (b) obesity (OBCD). These networks reveal the central role of inflammatory mediators like IL6 and TLR4, suggesting a link between metabolic disorders and heightened inflammatory responses during SARS-CoV-2 infection. Figure 3 depicts the PPI networks for genes shared between COVID-19 and (a) cardiovascular disease (CVD) and (b) cerebrovascular disease (CeVD), highlighting how IL6, TLR4, and CCL2 regulate inflammatory pathways that may exacerbate vascular complications in COVID-19 patients. To further explore the overlap of hub genes across all conditions, we constructed a comprehensive PPI network, as shown in Figure 4. This interaction network illustrates the overlap of hub genes identified across all six comorbidities and COVID-19, with a core set of four genes—CCL2, IL6, IL10, and TLR4—present across all diseases. The network underscores their functional interactions in cytokine signaling, immune modulation, and inflammation, suggesting their potential as biomarkers for disease severity and therapeutic targets. Additionally, Supplementary Figure S1 provides a visual comparison of common overlapping hub genes involved in cross-talk between COVID-19 and its comorbidities, emphasizing the shared molecular mechanisms driven by these genes. It highlights the overlap of hub genes across T1D, T2D, OBCD, HTN, CVD, CeVD, and COVID-19, with CCL2, IL6, IL10, and TLR4 consistently present in all conditions. The visualization, generated using tools like Venny or Interactivenn, emphasizes the shared molecular mechanisms driven by these genes, reinforcing their critical role in the interplay between COVID-19 and its comorbidities.
Previous studies on hub gene identification were conducted by Prasad et al. to better understand the innate immune response of the human body during COVID-19 infection [25]. In this study, we aimed to predict genes involved in COVID-19 and its comorbidities, including T1D, T2D, OBCD, CVD, HTN, and CeVD, using a bioinformatics approach. We extracted gene sets from several databases and used the ToppGene Suite to identify common genes with functional similarity related to COVID-19 and these comorbidities. The ToppGene Suite is a bioinformatics tool that excels in prioritizing candidate genes and performing gene list enrichment analysis based on functional annotations and protein interaction networks. It is particularly useful for identifying genes that share pathogenic mechanisms between COVID-19 and its comorbidities. By comparing the functional similarity of input genes with known SARS-CoV-2-related genes, ToppGene can prioritize genes likely involved in both COVID-19 and its comorbidities. The tool’s enrichment analysis reveals common biological processes and pathways, while its protein interaction network analysis uncovers key regulators central to shared pathogenic processes. This integrated approach enables researchers to efficiently identify and rank genes that may contribute to the molecular mechanisms underlying both COVID-19 and its comorbidities, aiding in understanding and treating these conditions.
We also analyzed networks for COVID-19 and its respective comorbidities and hub gene networks to examine gene interactions and determine correlations. After identifying common hub genes, we created a diseasome network (Figure 5) to illustrate the hub genes associated with one or more comorbidities. Our analyses indicated that four hub genes—CCL2, IL6, IL10, and TLR4—were correlated with all the studied comorbidities. To gain deeper insights, we performed gene enrichment analysis and KEGG pathway analysis using Enrichr, identifying biological functions and disease pathways related to these genes, as detailed in Table 1, Table 2 and Table 3 for biological processes, cellular components, and molecular functions, respectively. Table 1 summarizes the biological processes common among the hub genes of COVID-19 and its comorbidities, highlighting processes such as cellular response to lipopolysaccharide and inflammatory response, driven by CCL2, IL6, IL10, and TLR4. Table 2 outlines the cellular components, including the integral component of the plasma membrane, where IL6 and TLR4 are notably active. Table 3 details the molecular functions, such as cytokine activity, underscoring the roles of IL10, IL6, and CCL2 in immune regulation. These tables collectively demonstrate the involvement of the hub genes in cytokine and chemokine activity, critical for the immune response against SARS-CoV-2.
CCL2, or C-C motif chemokine 2 (also known as monocyte chemotactic protein 1 or MCP-1), is responsible for recruiting leukocytes to infected areas. It also contributes to the homeostatic circulation of leukocytes and exhibits chemotactic activity for monocytes and basophils. Interleukin-6 (IL6), a cytokine, aids in the maturation and differentiation of B cells and inflammation. Interleukin-10 (IL10) has pleiotropic effects in immunoregulation and inflammation, primarily functioning as an anti-inflammatory agent and enhancing B-cell survival. Toll-like receptor 4 (TLR4), while not a cytokine or chemokine, serves as a receptor for recognizing and activating immunity by detecting molecular patterns of infectious agents. It is noteworthy that COVID-19 is strongly associated with inflammation, a process the human body uses to respond to harmful stimuli such as bacteria and irritants through immune responses. Our study demonstrated that the four hub genes present in COVID-19 and its comorbidities are involved in cytokine and chemokine activity. Given their molecular functions, these genes are potential candidates for participating in the immune response against SARS-CoV-2 entry.
Inflammation plays a key role in COVID-19 due to a phenomenon known as a cytokine storm, which the SARS-CoV-2 virus induces when it binds to receptors. This results in an aggressive inflammatory response, where large amounts of pro-inflammatory cytokines are released in the body [27]. Consequently, CCL2, IL6, IL10 [28], and TLR4 [29] have been reported to be overexpressed in COVID-19 patients due to their roles as cytokines and chemokines. For instance, CCL2 has been identified as a biomarker for COVID-19 severity [30]. IL6, which is involved in human metabolism and autoimmune cell differentiation [31], is a key mediator in cytokine storms [32,33]. It is also associated with symptoms such as increased body temperature [33]. While the entry of SARS-CoV-2 through ACE2 receptors has been widely reported [34], Brandão et al. [29] suggested that the virus also binds to TLR4 after ACE2, exacerbating the immune response and damaging the body. Although IL10 is an anti-inflammatory cytokine, it can render T-cells ineffective in their anti-tumor roles. This excessive cytokine activity can overwhelm the immune system, leading to acute respiratory distress syndrome (ARDS), organ failure, and, in severe cases, death [35].
In addition to their involvement in cytokine storms, these genes are also implicated in various comorbidities, which may explain why individuals with these conditions have a higher risk of severe COVID-19 infection. T2D is often linked to insulin resistance, unlike T1D. CCL2 functions as an insulin-responsive gene, reducing insulin-stimulated glucose uptake [36]. IL6 promotes insulin resistance by interfering with insulin signaling in primary hepatocytes, which diminishes glucose uptake [37]. TLR4 increases insulin resistance and inflammation by being activated by ligands such as free fatty acids and lipopolysaccharides [38]. Although IL10 levels are generally low in T2D patients, the cytokine storm seen in COVID-19 might alter this pattern in those with diabetes as a comorbidity. Research also shows that T1D patients are at risk of infection. CCL2 promotes monocyte migration, leading to insulitis and islet destruction [39]. IL6 enhances T-cell responses in T1D [40]. Like in T2D, IL10 levels are typically lower in T1D patients, aligning with its role as an anti-inflammatory cytokine. TLR4 is involved in islet destruction in T1D, as observed by Nunes et al. [41].
Obesity (OBCD) is associated with many negative health outcomes and is closely related to T2D. The genes’ roles in T2D also apply to obesity. IL6, for example, affects systemic insulin sensitivity in obese patients [42]. Elevated levels of glycerol, non-esterified fatty acids, pro-inflammatory cytokines, and hormones in adipose tissue contribute to insulin resistance [43]. TLR4 is a key activator of the inflammatory response induced by obesity [44]. These genes are also involved in hypertension (HTN). Increased blood pressure is linked to elevated IL6 and leukocyte adhesiveness [45]. IL6 initiates an acute phase response by hepatocytes, causing C-reactive protein (CRP) synthesis and inflammation [46]. TLR4 induces inflammation and oxidative stress in the renal, cardiovascular, and central nervous systems. Nunes et al. suggested that inhibiting TLR4 could reduce these effects [47]. Higher levels of CCL2 are also found in hypertensive patients.
In cardiovascular disease (CVD), elevated levels of these genes increase the risk of death or myocardial infarction (MI). CCL2 is an inflammatory biomarker linked to heart failure severity [48], while TLR4 activates pro-inflammatory cytokine expression [49]. Elevated IL6 levels increase the risk of MI [50] and contribute to tissue damage in ARDS [51]. Though IL10 is anti-inflammatory, it reflects a pro-inflammatory state in patients with acute coronary syndrome (ACS) [52].
Lastly, these genes are involved in cerebrovascular disease (CeVD). TLR4 activates COX-2 via nuclear factor κ-B translocation, contributing to ischemic brain damage [53]. IL6 is a prognostic factor for ischemic stroke, with higher levels associated with more severe neurological damage [54]. CCL2 is implicated in blood–brain barrier (BBB) disruption, and CCL2 receptor inhibitors have been shown to be neuroprotective in ischemic stroke [55]. IL10 levels rise in response to brain injury. We acknowledge the limitations of our study.
It focuses on four hub genes in COVID-19 and the selected comorbidities. Although T1D, T2D, OBCD, CVD, HTN, and CeVD are common in COVID-19 patients, other comorbidities, such as chronic obstructive pulmonary disease (COPD), were not examined.
The genes CCL2, IL6, IL10, and TLR4 share mechanisms with SARS-CoV-2 in both COVID-19 and its comorbidities—including type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD). These shared mechanisms primarily involve their roles in immune response, inflammation, and cytokine signaling.
CCL2 is a chemokine crucial for recruiting monocytes, T-cells, and dendritic cells to sites of inflammation. In COVID-19, elevated levels of CCL2 contribute to the accumulation of immune cells in the lungs, exacerbating inflammation and potentially worsening disease severity. This chemokine also plays a significant role in the chronic inflammation associated with T2D and CVD, where it contributes to insulin resistance, atherosclerosis, and vascular inflammation. IL6, a pro-inflammatory cytokine, is markedly elevated in COVID-19 patients, particularly during the cytokine storm—a severe immune response that can lead to acute respiratory distress syndrome (ARDS) and multi-organ failure.
IL6 mediates fever, inflammation, and tissue injury in COVID-19. It is also implicated in the pathogenesis of T2D, OBCD, and HTN, where it promotes insulin resistance, inflammation, and endothelial dysfunction, thereby worsening symptoms in COVID-19 patients with these comorbidities. IL10, an anti-inflammatory cytokine, regulates immune responses by inhibiting the expression of pro-inflammatory cytokines. Although generally protective, its dysregulation in COVID-19 can contribute to immune system imbalance, potentially impairing effective viral clearance. In diseases like T1D and CVD, IL10’s role in modulating immune responses can become dysfunctional, leading to chronic inflammation and immune dysregulation. This dysfunction may exacerbate the severity of these conditions in COVID-19 patients.
TLR4 is a pattern recognition receptor that recognizes pathogen-associated molecular patterns (PAMPs). In COVID-19, TLR4 may initiate innate immune responses by recognizing viral components, thereby activating inflammatory pathways and contributing to cytokine release. In comorbid conditions such as CVD, HTN, and OBCD, TLR4 is associated with inflammation and activates immune responses that lead to vascular inflammation, insulin resistance, and metabolic dysregulation. All of these factors can worsen COVID-19 outcomes in patients with these underlying conditions. Together, these genes contribute to the immune dysregulation observed in COVID-19 and its comorbidities, amplifying disease severity through shared pathways involving inflammation, cytokine signaling, and immune response.
Our identification of CCL2, IL6, IL10, and TLR4 as hub genes aligns with previous genomic and biochemical studies that have implicated these genes in COVID-19 susceptibility and severity. For example, a GWAS study identified variants in IL6 and CCL2 associated with severe COVID-19 outcomes, particularly in patients with heightened inflammatory responses [12]. Similarly, TLR4 has been shown to modulate innate immune responses to SARS-CoV-2, with specific polymorphisms linked to differential disease severity [PMID: 37196358]. Furthermore, IL10 variants have been associated with immune dysregulation in COVID-19, influencing the balance between pro- and anti-inflammatory responses [16]. These studies corroborate our findings, suggesting that the hub genes identified in our analysis are critical players in the molecular mechanisms underlying COVID-19 and its comorbidities.
The hub genes identified in our study—CCL2, IL6, IL10, and TLR4—have been associated with specific genetic variants that influence COVID-19 susceptibility, severity, and immune response. For instance, polymorphisms in TLR4, such as rs4986790 (D299G) and rs4986791 (T399I), have been linked to altered immune responses in COVID-19 patients. The D299G variant reduces TLR4 signaling efficiency, potentially decreasing the inflammatory response but also impairing viral clearance, while T399I has been associated with increased susceptibility to severe infections [15]. In IL6, the -174 G/C polymorphism (rs1800795) is associated with elevated IL6 levels, contributing to severe inflammatory responses and cytokine storms in COVID-19 patients [12]. For CCL2, the -2518 A/G polymorphism (rs1024611) enhances CCL2 expression, promoting excessive monocyte recruitment and exacerbating inflammation in both COVID-19 and comorbidities like T2D and CVD [16]. Similarly, IL10 polymorphisms, such as -1082 G/A (rs1800896), influence IL10 production, with the A allele linked to lower IL10 levels and impaired anti-inflammatory responses, potentially worsening outcomes in COVID-19 patients with comorbidities [13]. Notably, the roles of CCL2, IL6, IL10, and TLR4 in COVID-19 severity complement the functions of other susceptibility genes, such as ACE2 and TMPRSS2, which facilitate SARS-CoV-2 entry [12], and IFITM3, which modulates interferon-mediated antiviral responses [14]. For instance, TLR4 and IL6 may interact with interferon signaling pathways regulated by IFITM3 and IRF7, amplifying inflammatory responses in severe COVID-19 cases. Similarly, HLA variants associated with immune regulation [13] may influence the expression or activity of IL10, affecting the balance of pro- and anti-inflammatory responses. These interconnections suggest that the hub genes identified in our study are part of a broader molecular network driving COVID-19 pathogenesis, particularly in patients with comorbidities. These genetic variants, summarized in Table 4, highlight the role of hub genes in modulating disease severity and provide potential targets for personalized therapeutic strategies. Targeting these polymorphisms, such as IL6 rs1800795 with anti-IL6 therapies (e.g., tocilizumab) or TLR4 rs4986790 with immunomodulatory agents, could offer personalized treatment strategies for COVID-19 patients with comorbidities, pending validation of their functional roles. However, the functional impact of these polymorphisms requires further validation through biochemical and clinical studies to confirm their roles in disease pathogenesis.

4. Limitations

While our bioinformatics approach has identified CCL2, IL6, IL10, and TLR4 as common hub genes in COVID-19 and its comorbidities, this study is limited by its reliance on computational methods. The predicted hub genes and their associated pathways were derived from database-driven analyses and interactome networks, which, while robust, require validation through functional biochemical studies. In silico predictions, although valuable for hypothesis generation, cannot fully capture the complex biological interactions occurring in vivo. For instance, the roles of these hub genes in cytokine signaling and inflammation must be confirmed through experimental approaches, such as gene expression profiling, protein interaction assays, and clinical studies in COVID-19 patients with comorbidities. Our study identified shared genetic mechanisms, including hub genes (CCL2, IL6, IL10, TLR4), between COVID-19 and specific comorbidities (T1D, T2D, OBCD, CVD, HTN, CeVD), but did not explore other conditions such as chronic obstructive pulmonary disease (COPD) or chronic kidney disease (CKD), which may limit the broader applicability of our findings. To enhance the generalizability of these results, future research should include a wider range of comorbidities to provide a more comprehensive understanding of shared molecular pathways in COVID-19. Similarly, emerging evidence suggests a connection between COVID-19 and neurological conditions like Alzheimer’s disease, indicating shared inflammatory and immune pathways that could be explored in future studies [56]. Additionally, laboratory-based studies are essential to validate our computational predictions and elucidate the functional roles of identified genetic variants, ensuring their biological relevance. Furthermore, incorporating ethnic-specific genomic data, age-related clinical outcomes, and environmental factors will be critical to assess the relevance of these hub genes across diverse populations, addressing disparities and informing targeted interventions. Despite its limitations, the robustness of our methodology and the novelty of our findings position this study as a pivotal step toward understanding the genetic underpinnings of COVID-19 severity, paving the way for future research to build upon these insights.

5. Conclusions

COVID-19 remains a global concern, as it continues to claim lives daily. Our study identified genes common to COVID-19 and the comorbidities T1D, T2D, OBCD, CVD, HTN, and CeVD. Using computational approaches such as interactome analysis of protein–protein networks and functional enrichment analysis, we discovered four common genes: CCL2, IL6, IL10, and TLR4. These genes are involved in the immune response and play a key role in COVID-19, particularly by contributing to the cytokine storm, a stimulus induced by SARS-CoV-2. Additionally, these genes are linked to the comorbidities, increasing patients’ risk of severe COVID-19 infection. The genetic variants identified in these hub genes, such as IL6 rs1800795 and TLR4 rs4986790, suggest potential targets for precision medicine approaches, such as anti-cytokine therapies or TLR4 inhibitors, to mitigate severe COVID-19 outcomes in comorbid populations. Further studies focused on genomic changes in COVID-19 patients with comorbidities could provide critical insights into the relationship between these conditions. Ultimately, these findings may offer valuable information for future research and could potentially aid in the development of vaccines to counteract the effects of COVID-19.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/covid5070105/s1, Figure S1: Comparison and identification of common overlapping hub genes involved in cross talk between COVID-19 and comorbidities.

Author Contributions

Conceptualization, S.K. and K.J.S.K.; methodology, S.K. and J.-J.W.; software, S.K. and J.-J.W., formal analysis, S.K. and J.-J.W.; writing—review and editing, S.K. and K.J.S.K.; supervision, S.K., and K.J.S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study was partially supported by grants from the National Science and Technology Council, Taiwan (NSTC 111-2313-B-005-052- MY3). The funding body did not have any role in the design of the study and the collection, analysis, and interpretation of the data, and in writing the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Protein–protein interaction of common genes of COVID-19 and (a) type 1 diabetes and (b) type 2 diabetes. This figure presents the PPI networks of genes that are common between COVID-19 and (a) type 1 diabetes (T1D) and (b) type 2 diabetes (T2D). The networks were generated using Cytoscape with data obtained from the STRING database. In each network, green nodes represent genes shared between COVID-19 and diabetes, while red nodes indicate the key hub genes identified by cytoHubba’s maximal clique centrality (MCC) ranking method. The thickness of the edges represents the strength of the interaction, with thicker lines indicating stronger associations between proteins. The highlighted hub genes (e.g., CCL2, IL6, IL10, and TLR4) play significant roles in immune regulation and cytokine signaling, contributing to both COVID-19 severity and diabetes pathogenesis.
Figure 1. Protein–protein interaction of common genes of COVID-19 and (a) type 1 diabetes and (b) type 2 diabetes. This figure presents the PPI networks of genes that are common between COVID-19 and (a) type 1 diabetes (T1D) and (b) type 2 diabetes (T2D). The networks were generated using Cytoscape with data obtained from the STRING database. In each network, green nodes represent genes shared between COVID-19 and diabetes, while red nodes indicate the key hub genes identified by cytoHubba’s maximal clique centrality (MCC) ranking method. The thickness of the edges represents the strength of the interaction, with thicker lines indicating stronger associations between proteins. The highlighted hub genes (e.g., CCL2, IL6, IL10, and TLR4) play significant roles in immune regulation and cytokine signaling, contributing to both COVID-19 severity and diabetes pathogenesis.
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Figure 2. Protein–protein interaction of common genes of COVID-19 and (a) hypertension and (b) obesity. This figure illustrates the PPI networks of shared genes between COVID-19 and two major comorbid conditions: (a) hypertension (HTN) and (b) obesity (OBCD). Each network was constructed using STRING and visualized in Cytoscape. The green nodes denote genes common to both COVID-19 and the respective comorbidity, while red nodes highlight hub genes ranked using cytoHubba. The network topology reveals key interactions, indicating potential mechanisms by which obesity and hypertension may exacerbate COVID-19 severity. The central role of inflammatory mediators such as IL6 and TLR4 suggests a link between metabolic disorders and heightened inflammatory responses during SARS-CoV-2 infection.
Figure 2. Protein–protein interaction of common genes of COVID-19 and (a) hypertension and (b) obesity. This figure illustrates the PPI networks of shared genes between COVID-19 and two major comorbid conditions: (a) hypertension (HTN) and (b) obesity (OBCD). Each network was constructed using STRING and visualized in Cytoscape. The green nodes denote genes common to both COVID-19 and the respective comorbidity, while red nodes highlight hub genes ranked using cytoHubba. The network topology reveals key interactions, indicating potential mechanisms by which obesity and hypertension may exacerbate COVID-19 severity. The central role of inflammatory mediators such as IL6 and TLR4 suggests a link between metabolic disorders and heightened inflammatory responses during SARS-CoV-2 infection.
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Figure 3. Protein–protein interaction of common genes of COVID-19 and (a) cardiovascular and (b) cerebrovascular diseases. This figure depicts the PPI networks of genes shared between COVID-19 and (a) cardiovascular disease (CVD) and (b) cerebrovascular disease (CeVD). Nodes represent genes, and edges indicate functional or physical interactions between proteins. The red nodes correspond to hub genes identified via MCC ranking, while green nodes represent additional shared genes. These interactions provide insights into how SARS-CoV-2 infection may impact vascular health, particularly through inflammatory pathways regulated by IL6, TLR4, and CCL2. The strong connectivity of these hub genes highlights their potential as therapeutic targets for reducing cardiovascular complications in COVID-19 patients.
Figure 3. Protein–protein interaction of common genes of COVID-19 and (a) cardiovascular and (b) cerebrovascular diseases. This figure depicts the PPI networks of genes shared between COVID-19 and (a) cardiovascular disease (CVD) and (b) cerebrovascular disease (CeVD). Nodes represent genes, and edges indicate functional or physical interactions between proteins. The red nodes correspond to hub genes identified via MCC ranking, while green nodes represent additional shared genes. These interactions provide insights into how SARS-CoV-2 infection may impact vascular health, particularly through inflammatory pathways regulated by IL6, TLR4, and CCL2. The strong connectivity of these hub genes highlights their potential as therapeutic targets for reducing cardiovascular complications in COVID-19 patients.
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Figure 4. Protein–protein interaction network for COVID-19 with comorbidities. This interaction network illustrates the overlap of hub genes identified across all six comorbidities and COVID-19. It shows the number of genes shared between each condition, with a core set of four genes (CCL2, IL6, IL10, and TLR4) present across all diseases. The accompanying network highlights functional interactions among these genes, reinforcing their role in cytokine signaling, immune modulation, and inflammation. The findings suggest that these common hub genes may serve as potential biomarkers for disease severity and as targets for therapeutic intervention.
Figure 4. Protein–protein interaction network for COVID-19 with comorbidities. This interaction network illustrates the overlap of hub genes identified across all six comorbidities and COVID-19. It shows the number of genes shared between each condition, with a core set of four genes (CCL2, IL6, IL10, and TLR4) present across all diseases. The accompanying network highlights functional interactions among these genes, reinforcing their role in cytokine signaling, immune modulation, and inflammation. The findings suggest that these common hub genes may serve as potential biomarkers for disease severity and as targets for therapeutic intervention.
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Figure 5. Comparison and identification of common overlapping hub genes involved in cross talk between COVID-19 and comorbidities.
Figure 5. Comparison and identification of common overlapping hub genes involved in cross talk between COVID-19 and comorbidities.
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Table 1. Biological process common among hub genes of COVID-19 and its comorbidities.
Table 1. Biological process common among hub genes of COVID-19 and its comorbidities.
TermOverlapp-ValueAdjusted p-ValueOdds
Ratio		Combined ScoreGenes
Cellular response to molecule of bacterial origin (GO:0071219)4/842.89 × 10−101.48 × 10−6238.09525229.299IL10; IL6; CCL2; TLR4
Cellular response to lipopolysaccharide (GO:0071222)4/914.01 × 10−101.02 × 10−6219.78024755.435IL10; IL6; CCL2; TLR4
Response to lipopolysaccharide (GO:0032496)4/1553.47 × 10−95.90 × 10−6129.03232513.437IL10; IL6; CCL2; TLR4
Cellular response to lipid (GO:0071396)4/1786.07 × 10−97.74 × 10−6112.35962125.912IL10; IL6; CCL2; TLR4
Inflammatory response (GO:0006954)4/2522.46 × 10−82.51 × 10−579.365081390.476IL10; IL6; CCL2; TLR4
Regulation of interleukin-6 production (GO:0032675)3/423.44 × 10−82.92 × 10−5357.14296137.679IL10; IL6; TLR4
Cellular response to oxygen-containing compound (GO:1901701)4/2743.45 × 10−82.51 × 10−572.99271254.253IL10; IL6; CCL2; TLR4
Response to molecule of bacterial origin (GO:0002237)3/14.55 × 10−72.90 × 10−4153.06122235.255IL10; IL6; TLR4
Positive regulation of nitric-oxide synthase biosynthetic process (GO:0051770)2/91.08 × 10−66.12 × 10−41111.11115265.62CCL2; TLR4
Positive regulation of macromolecule biosynthetic process (GO:0010557)3/1421.39 × 10−67.12 × 10−4105.63381424.271IL6; CCL2; TLR4
Table 2. Cellular components common among hub genes of COVID-19 and its comorbidities.
Table 2. Cellular components common among hub genes of COVID-19 and its comorbidities.
TermOverlapp-ValueAdjusted p-ValueOdds
Ratio		Combined ScoreGenes
Integral component of plasma membrane (GO:0005887)2/14630.02904516.8352724.18936IL6; TLR4
Endoplasmic reticulum lumen (GO:0005788)1/2700.05292118.5185254.42546IL6
Perinuclear region of
cytoplasm (GO:0048471)		1/3780.073489113.2275134.53206TLR4
Table 3. Molecular function common among hub genes of COVID-19 and its comorbidities.
Table 3. Molecular function common among hub genes of COVID-19 and its comorbidities.
TermOverlapp-ValueAdjusted p-ValueOdds RatioCombined ScoreGenes
Cytokine activity (GO:0005125)3/1550.000001820.0020996.774191279.258IL10; IL6; CCL2
Growth factor activity (GO:0008083)2/690.00007010.040323144.92751386.386IL10; IL6
Growth factor receptor binding (GO:0070851)2/920.0001250.047893108.6957977.0179IL10; IL6
Cytokine receptor binding (GO:0005126)2/1370.0002770.079772.9927597.9256IL10; IL6
Interleukin-6 receptor binding (GO:0005138)1/70.0013990.322128714.28574694.11IL6
CCR chemokine receptor binding (GO:0048020)1/380.0075791131.5789642.4203CCL2
Chemokine activity (GO:0008009)1/460.0091691108.6957509.9933CCL2
Chemokine receptor binding (GO:0042379)1/490.0097651102.0408472.3453CCL2
Phosphotransferase activity, alcohol group as acceptor (GO:0016773)1/2540.049844119.6850459.03268CCL2
Protein heterodimerization activity (GO:0046982)1/2650.051959118.8679255.79795TLR4
Kinase activity (GO:0016301)1/2800.054839117.8571451.84569CCL2
Protein kinase activity (GO:0004672)1/5130.09872619.74658922.56729CCL2
Table 4. Key polymorphisms in hub genes associated with COVID-19 and comorbidities. This table summarizes genetic variants in CCL2, IL6, IL10, and TLR4, their functional effects, and their associations with disease severity in COVID-19 and comorbidities (T1D, T2D, CVD), highlighting potential targets for therapeutic intervention.
Table 4. Key polymorphisms in hub genes associated with COVID-19 and comorbidities. This table summarizes genetic variants in CCL2, IL6, IL10, and TLR4, their functional effects, and their associations with disease severity in COVID-19 and comorbidities (T1D, T2D, CVD), highlighting potential targets for therapeutic intervention.
GenePolymorphismEffect on FunctionAssociation with COVID-19/ComorbiditiesReference
TLR4rs4986790 (D299G)Reduced TLR4 signaling, impaired viral clearanceIncreased susceptibility to severe COVID-19[15]
TLR4rs4986791 (T399I)Altered immune responseSevere COVID-19 outcomes[15]
IL6rs1800795 (−174 G/C)Increased IL6 expressionCytokine storm, severe COVID-19[12]
CCL2rs1024611 (−2518 A/G)Enhanced CCL2 expressionInflammation in COVID-19, T2D, CVD[16]
IL10rs1800896 (−1082 G/A)Reduced IL10 productionImmune dysregulation in COVID-19, T1D[13]
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Kumar, S.; Wee, J.-J.; Kumar, K.J.S. Identification of Common Hub Genes in COVID-19 and Comorbidities: Insights into Shared Molecular Pathways and Disease Severity. COVID 2025, 5, 105. https://doi.org/10.3390/covid5070105

AMA Style

Kumar S, Wee J-J, Kumar KJS. Identification of Common Hub Genes in COVID-19 and Comorbidities: Insights into Shared Molecular Pathways and Disease Severity. COVID. 2025; 5(7):105. https://doi.org/10.3390/covid5070105

Chicago/Turabian Style

Kumar, Suresh, Jia-Jin Wee, and K. J. Senthil Kumar. 2025. "Identification of Common Hub Genes in COVID-19 and Comorbidities: Insights into Shared Molecular Pathways and Disease Severity" COVID 5, no. 7: 105. https://doi.org/10.3390/covid5070105

APA Style

Kumar, S., Wee, J.-J., & Kumar, K. J. S. (2025). Identification of Common Hub Genes in COVID-19 and Comorbidities: Insights into Shared Molecular Pathways and Disease Severity. COVID, 5(7), 105. https://doi.org/10.3390/covid5070105

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