|Year : 2022 | Volume
| Issue : 2 | Page : 79-91
Identification of unique immune response expression profiles to SARS-CoV-2 in non-small cell lung cancer using systems immunology approach
Saba Al Heialy1, Mahmood Yaseen Hachim2, Ibrahim Yaseen Hachim3, Rifat Hamoudi3, Qutayba Hamid4
1 College of Medicine, Mohammed bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates; Meakins-Christie Laboratories, Research Institute of the McGill University Health Center, Montreal, Quebec, Canada
2 College of Medicine, Mohammed bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
3 Sharjah Institute for Medical Research, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
4 Sharjah Institute for Medical Research, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates; Meakins-Christie Laboratories, Research Institute of the McGill University Health Center, Montreal, Quebec, Canada
|Date of Submission||17-Feb-2022|
|Date of Decision||04-Apr-2022|
|Date of Acceptance||05-Apr-2022|
|Date of Web Publication||29-Apr-2022|
Sharjah Institute for Medical Research, College of Medicine, University of Sharjah, Sharjah
Source of Support: None, Conflict of Interest: None
Background: COVID-19 severity and mortality are higher in patients with lung cancer due to pulmonary complications. Understanding the mechanisms of SARS-COV-2 effect on lung cancer cells in comparison to healthy lung cells can improve our knowledge of the disease biology to discover new therapeutic targets with the aim of improving the management protocols. Methods: We aimed to investigate the immune response signature generated from COVID-19-infected NSCLC patients and compare with noninfected patients. To achieve this, publicly available transcriptomic data of lung adenocarcinoma cancer cells A549 versus healthy lung epithelium which were SARS-COV-2-infected and mock-infected were retrieved and reanalyzed to identify differentially expressed genes (DEGs) that are dysregulated in SARS-COV-2-infected A549. Identified genes were explored for enriched pathways and further validated in silico for their expression in larger NSCLC lung samples. C57BL/6J mice infected with MA15 (mouse-adapted SARS-CoV) were used to confirm the findings. Results: A total of 7852 DEGs were identified between A549 (mock and SARS-COV-2 infected) compared to healthy epithelial cells (mock and SARS-COV-2 infected). On the contrary, 142 genes were DEGs between all mocked-infected cells (healthy and cancer) versus SARS-COV-2 infected (healthy and cancer). Those 142 genes were intersected with DEGs from the first step and were shown to be involved in cytokine-mediated signaling pathway and lymphocyte activation. A549-infected cells upregulated (IL11, RBCK1, CEBPD, EBI3, and ISG15) to a higher proportion but downregulated RELB compared to the healthy epithelium. Most of the genes (Nr1h4, Ebi3, Snai2, IL2rb, IL11, Clec4e, Cebpd, and Relb) were differentially expressed in the lung of infected mice. In silico validation confirm that IL11 expression is higher in lung adenocarcinoma compared to healthy controls. COVID-19 infection in NSCLC patients lead to the activation of specific cytokines. Conclusions: Our analysis showed IL11 to be the most differentially expressed between cancer and non-cancer patients and was associated with poor prognosis suggesting that COVID-19 infection in cancer patients leads to the synergistic increase in expression of CD4+ T cells, M1 macrophages, and follicular helper T cells.
Keywords: Bioinformatics, COVID-19, cytokines, immune responses, lung cancer, SARS-CoV-2, systems immunology
|How to cite this article:|
Al Heialy S, Hachim MY, Hachim IY, Hamoudi R, Hamid Q. Identification of unique immune response expression profiles to SARS-CoV-2 in non-small cell lung cancer using systems immunology approach. Adv Biomed Health Sci 2022;1:79-91
|How to cite this URL:|
Al Heialy S, Hachim MY, Hachim IY, Hamoudi R, Hamid Q. Identification of unique immune response expression profiles to SARS-CoV-2 in non-small cell lung cancer using systems immunology approach. Adv Biomed Health Sci [serial online] 2022 [cited 2022 May 25];1:79-91. Available from: https://www.abhsjournal.net/text.asp?2022/1/2/79/344318
| Background|| |
After 3 years of COVID-19 outbreak (SARS-CoV-2), the pandemic is still widely spreading globally and reaching to over 400 million cases with more than five million reported death worldwide . According to initial reports, 80% of patients with this disease usually suffer only from mild to moderate disease; in comparison, 14% will develop severe illness, and 6% will have the critical form of the disease that requires intensive care. Age and presence of comorbidities were associated with worse outcomes . One of the subgroups of patients that are considered as highly vulnerable group is cancer patients. Indeed, a recent report confirms that patients previously diagnosed with cancer had a higher risk of severe disease manifestation compared to patients without cancer . Although the general population had only a 2.3% fatality rate, this percentage increased to 5.6% in cancer patients . The systemic immunosuppressive status, as well as the anticancer treatment, can play a major role in the high mortality and morbidity status of these patients.
Lung cancer patients were reported to be at high risk of pulmonary complications related to SARS-CoV2 infection . NSCLC represents the majority of lung cancer, and it includes the most common subtypes: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) . NSCLC is known to set an immunosuppressive tumor microenvironment due to large numbers of regulatory T (Treg) cells to inhibit T cell proliferation and promote tumor growth . This cancer-induced local immunosuppression and systemic immunosuppression by treatment and by the disease might make patients more susceptible to lethal disease.
Despite the extensive efforts made to investigate the biological, clinical as well as the radiological findings associated with COVID-19, only a few reports were able to investigate the pathological changes. This was attributed to the lower rates of performing invasive diagnostic tests due to the severity of the disease and associated high risk of viral transmissibility with sampling procedures, as well as shortage of medical staff . However, few reports were able to investigate those changes from autopsy samples or biopsies [9,10]. Those reports showed evidence of diffuse alveolar damage, in addition to the presence of proteinaceous exudates. Other features of acute respiratory distress syndrome were also seen, including pulmonary edema, pneumocyte desquamation, hyaline membrane formation, interstitial mononuclear inflammatory infiltrates as well as the presence of multinucleated giant cells.
Cancer patients who tested positive for COVID-19 made up 1% of the total number of COVID-19 patients in Wuhan, China. Lung cancer was the most common cancer among these patients . Interestingly, one report described pathological changes in the early phase of COVID-19-associated pneumonia in patients with lung cancer. This report found apart from the tumor, the pathological manifestation is similar to that observed in patients with no lung cancer diagnosis . This great similarity between patients with and without pre-existing cancers in the tissue level highlight the need of more molecular level in-depth analysis for better understanding of the pathways and mechanisms used by COVID-19 pathogen to attack and target those highly vulnerable groups. Understanding those mechanisms is an essential step in improving our knowledge of the disease biology, which might help in the discovery of new therapeutic targets and molecules and improve the management protocols through specifically tailoring them according to the patient’s specific comorbidity status. Based on that, we were interested to see if the healthy lung epithelial cells showed different responses compared to lung cancer cells when both are infected with respiratory viruses versus mock-infected samples.
| Materials and methods|| |
We reanalyzed the publicly available transcriptomic dataset (GSE147507) recently uploaded to the Gene Expression Omnibus (GEO) [11,12]. In this dataset, independent biological triplicates of primary human lung epithelium (NHBE) and A549 (adenocarcinoma human alveolar basal epithelial cells) were mock-treated or infected with SARS-CoV-2 then subjected to RNA-Sequencing [Figure 1].
|Figure 1: Scheme representing the methodology used to identify DEGs specific to A549 infected with SARS-COV-2 compared to healthy epithelial cells.|
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Reanalysis of microarray data
The raw read counts were retrieved then subjected to two-way analysis to determine the differentially expressed genes (DEGs) between the healthy (infected and noninfected groups) versus cancer cells (infected and noninfected groups). Through this analysis, we identified the core biomarkers that differ between the two sets.
Gene expression analysis
DEGs were identified using Limma Bioconductor package in R and those with P < 0.05 and two fold up or down-regulation between the two groups in each step. AltAnalyze software for Comprehensive Transcriptome Analysis was used to a generated heatmap of the top DEGs . The DEGs from the two comparisons, healthy (infected and noninfected groups) versus cancer cells (infected and noninfected groups), were intersected.
The resultant genes were explored for any common pathways using Metascape online tool for gene ontology (http://metascape.org)  and validated using classical statistics applied to the raw data.
In order to document that the identified genes are biologically relevant, we reanalyzed the publicly available transcriptomic dataset (GSE64660) of C57BL/6J mice infected with MA15 (mouse-adapted SARS-CoV) by the intranasal route. To validate the expression of IL11 in adenocarcinoma compared to healthy controls, we used LCE web portal to explore gene expression and clinical associations in lung cancer  using TCGA_LUAD_2016 dataset . We searched DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) project tool (https://dice-database.org/) to compare the expression of IL11RA in different immune cells. Overall survival of patients with LUAD was divided into IL11 high expressing cancers (upper quartile) and IL11 low expressing cancers (lower quartile) using Kaplan- Meier Plotter tool (http://kmplot.com/analysis/) .
| Results|| |
Shared genes in cytokine-mediated and lymphocyte activation signaling pathway were differentially expressed genes between A549 and healthy bronchial epithelium
Using Limma Bioconductor package in R our analysis revealed 7852 DEGs that showed two fold up or down-regulation with P < 0.05 between LUAD cancer cells A549 (mock and SARS-COV-2 infected) compared to healthy epithelial cells (mock and SARS-COV-2 infected). On the contrary, clustering all noninfected mock cells as one group (healthy epithelial and lung cancer cells) and compare it to all SARS-COV-2-infected cells (healthy epithelial and lung cancer cells) using the same method we used above revealed 142 DEGs between the two groups.
Interestingly, intersection between the DEGs obtained from the first step and second revealed that all the 142 genes differentially between SARS-COV-2-infected and noninfected cells were also differentially expressed between LUAD cancer cells and healthy epithelial cells
The top DEGs in healthy versus cancerous cells in all conditions of SARS-COV-2 infection and with mock were identified, and they clustered the two groups separately [Figure 2].
|Figure 2: Heatmap, clustering of primary human lung epithelium (NHBE), and A549 infected versus noninfected (mock-infected cells). The publicly available transcriptomics dataset (GSE147507) was used. All healthy cells (infected and noninfected) were considered as one group (EPI), whereas all A549 (infected and noninfected) were in another group (index).|
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To have a detailed analysis of the pathways where the 142 DEGs intersected, the DEGs were uploaded to metascape online tool to explore the details of the top pathways shared. The genes related to the pathways are listed in [Table 1].
|Table 1: Heatmap of top gene ontology (GO) of the top DEGs in normal epithelium versus A549 mock or SARS-COV-2 infected.|
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Interestingly the top pathways (at least 10 genes involved) were related to cytokine-mediated signaling pathway and lymphocyte activation signaling pathway like (IL11, PRKCZ, CEBPD, COL1A2, EDN2, IL2RB, SLIT2, RBCK1, TRIM31, MEF2C, PAK3, TNFRSF18, IL1RL2, PGLYRP1, DLL4, RELB, SNAI2, TNFRSF4, NFRSF14, ISG15, NR1H4, EBI3, HHLA2, CLEC4E, UBASH3A, and CPTP) and were further explored [Figure 3] and [Figure 4]. The raw expression of the selected genes were explored and genes showing at least 10 transcripts per sample were filtered (IL11, PRKCZ, CEBPD, EDN2, RBCK1, TRIM31, MEF2C, IL1RL2, DLL4, RELB, SNAI2, TNFRSF14, ISG15, NR1H4, EBI3, and CLEC4E) [Table 1S].
|Figure 3: Differentially expressed genes (DEGs) between A549 and healthy bronchial epithelium (HNBE and) at baseline (A). Heatmap of top Gene Ontology (GO) of the top DEGs in normal epithelium versus A549 at baseline before infection (B). The bars represent the –log10 pf the adjusted p-value for each pathway.|
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|Figure 4: Heatmap of gene expression of Cytokine-mediated signaling pathway and lymphocyte activation. IL11, PRKCZ, CEBPD, EDN2, RBCK1, TRIM31, MEF2C, IL1RL2, DLL4, RELB, SNAI2, TNFRSF14, ISG15, NR1H4, EBI3, and CLEC4E were DEGs between healthy epithelium and A549 cells.|
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Differentially expressed genes between A549 and healthy bronchial epithelium (HNBE and) at baseline
To investigate the immune response difference between HBE and A549, at baseline before infection, which might affect infection susceptibility and immune response, we identified DEG between HNBE and A549 without infections from the same dataset. A total of 2419 genes were differentially expressed between the two cells (adjP < 0.05 and logFC >2 or <–2) [Figure 3A].
Next, we perform pathway enrichment analysis to investigate the top DEGs enriched pathways. DEGs (adjP < 0.05 and logFC >5 or <–5) were examined using metascape online tool and three major pathways related to immune response were in the top list of enriched pathways (chemotaxis, regulation of cytokine production and inflammatory response) [Figure 3B] and [Table 2].
|Table 2: The genes in each pathway that were enriched in the top DEGs in normal epithelium versus A549 at baseline before infection.|
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A549 response to SARS-CoV-2 infection differs to that in healthy epithelium
Next, we compared the ratio of gene expression of each of the selected genes in healthy or lung cancer A549 cells infected with SARS-CoV-2 compared the noninfected mock controls of each (healthy epithelial and lung cancer cells) [Table 3]. Interestingly, A549-infected cells upregulate IL11, RBCK1, CEBPD, EBI3 and ISG15 to a higher proportion but downregulate RELB when compared to healthy epithelium [Figure 4] and [Figure 5]. This data suggests dysregulation in the immune response markers of A549 cells compared to healthy cells.
|Table 3: Ratio of gene expression of selected genes in healthy (HE) and A549 cells.|
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|Figure 5: Heatmap of top gene ontology (GO) of the top DEGs in normal epithelium versus A549. The bars represent the –log10 pf the adjusted p-value for each pathway.|
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Certain identified genes showed upregulation in lungs of mice infected with SARS-CoV
Further validation was made through reanalyzing the publicly available transcriptomic dataset (GSE64660) of C57BL/6J mice infected with a mouse adapted (MA15) severe acute respiratory syndrome (SARS) coronavirus (CoV) (mouse-adapted SARS-CoV) by the intranasal route. The results showed that most of the genes (Nr1h4, Ebi3, Snai2, IL2rb, IL11, Clec4e, Cebpd, and Relb) were differentially expressed in the lung of infected mice [Figure 6] and [Table 4].
|Figure 6: Gene Expression of identified genes in the publicly available transcriptomic dataset (GSE64660) of C57BL/6J mice infected with MA15 (mouse-adapted SARS-CoV) by the intranasal route.|
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IL11 expression is higher in lung adenocarcinoma compared to healthy controls and its receptor (IL11R) is abundant in CD4 T cells
Our different cell line and mouse models used above showed IL11 as one of the core differential expressed genes between infected and noninfected cells as well as normal versus cancer cells. For that reason, we next try to explore the gene expression levels and clinical associations of IL11 in human clinical samples. To achieve this, we next investigated LCE web portal  using TCGA_LUAD_2016 dataset , which represent comprehensive molecular profiling of 517 adenocarcinoma of the lung compared to adjacent normal samples. As expected, IL11 expression was higher in LUAD compared to adjacent normal samples [Figure 7].
|Figure 7: The expression of IL11 in adenocarcinoma compared to healthy controls using LCE web portal to explore gene expression and clinical associations in lung cancer in TCGA_LUAD_2016 dataset.|
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Next, and due to the fact that, in the shortlisted genes upregulated in lungs of SARS-CoV-infected mouse, IL11 was related to secreted interleukins, we explored if A549 by upregulating those genes can recruit certain immune cells to the region. We searched DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) project tool (https://dice-database.org/) to compare the expression of IL11RA in different immune cells. CD4+ TH2 and Treg showed the highest level of IL11RA and might be affected by cancer-induced IL11 [Figure 8].
|Figure 8: Mean Expression (TPM) of IL11RA using DICE (database of immune cell expression, expression quantitative trait loci (eQTLs), and epigenomics) project tool.|
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IL11 expression is correlated positively with Mast cells activated, Macrophages M1, and T cells follicular helper
In order to further understand the correlation between higher IL11 in adenocarcinoma of the lung and immune cell profile, we retrieved the TCGA_LUAD_2016 dataset  then uploaded the normalized RNAseq gene expression to CIBERSORT (https://cibersort.stanford.edu/) to quantify immune cell fractions from bulk lung tissue gene expression profile. Interestingly, IL11 expression showed significant positive correlation with activated mast cells, macrophages M1, and T cells follicular helper and negative correlation with T cells CD4 memory resting and macrophages M2 [Table 5].
Higher IL11 expression is a marker of worse patient outcome in patients with lung adenocarcinoma
Our previous result highlight a distinct immune profile of adenocarcinoma lung cancers compared to squamous cell carcinoma with a significant enrichment of those tumors with immune cell infiltration. For that reason and for better understating of the role of immune cells regulators like IL11, which might be used by the SARS-COV-2 to enhance the lung damage, we next investigate the effect of upregulation of such factors on lung cancer patient outcome presented as overall survival. [Figure 9] indicates that patients with higher IL11 expression showed decreased overall survival over a 200 month period compared to patients with lower IL11expression,This indicates that IL11 is associated with poor prognosis and highlight the possible use of such factors in inducing the lung damage in SARS-COV-2 infections.
|Figure 9: Profiling of gene expression in relation to survival. Two probes for the IL11 gene were used to analyze the survival using Kaplan–Meier plotter.|
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| Discussion|| |
Reports from China showed that COVID-19 infection has a substantial consequence in patients with cancer receiving anti-tumor treatments . In a recent paper, Tian et al. described the pulmonary pathology of two patients who underwent lobectomy for lung cancer before showing clinical symptoms of COVID-19 pneumonia . The authors described reactive hyperplasia of pneumocytes, a sensitive pathological finding that indicates alveolar injury as a reparative response to lost cells that occur a few days after the acute insult . In another case report, a patient with LUAD cancer infected with COVID-19 showed atypical lung feature on chest CT which was predominantly diffuse involvement instead of the typical subpleural distribution . The search for unique pathological markers that might tell us what makes patients with lung cancer more vulnerable or have a worse prognosis in response to infection is essential. Based on that, we were interested to see if the healthy lung epithelial cells showed different responses than lung cancer cells when both are infected with SARS coronavirusesversus mock-infected samples in terms of DEGs.
Interestingly our reanalysis showed that the top pathways were related to cytokine-mediated signaling pathway and lymphocyte activation. In general, cancers are known to release soluble growth factors and chemoattractants that mediate an inflammatory environment . It was documented earlier that solubilized proteins from a LUAD cell line (A549) and from lung tumors induce higher circulating levels of inflammatory cytokines such as IL-6 . Although, IL-6 showed anti-apoptotic cancer inhibitory effects, it can promote cancer development which is opposite to the action of virus induced interferons . By potentiating virus-induced apoptosis, interferons are considered anti-oncogenic .
The bile acid receptor farnesoid X receptor (FXR; NR1H4), identified in our analysis, is an essential regulator of bile acid, lipid metabolism, and glucose homeostasis. Dysfunction of NR1H4/FXR will lead to elevated serum glucose with impaired tolerance to insulin . NR1H4/FXR, when activated, actively suppresses autophagic gene promoters to regulate autophagy in response to nutrient status . NR1H4 has a significant role in regulating the autophagy-ciliogenesis axis , which functions as sensory and signaling organelles . In the context of the lung, the ciliary beating of airway epithelial cells constitutes an integral part of the mucociliary transport apparatus of the innate immune response , FXR induction of ciliogenesis degradation by autophagy can be part of the viral pathogenic factor and can exaggerate the infection and inflammation.
FXR has an anti-inflammatory effect on lipopolysaccharide (LPS)-induced acute lung injury by controlling the up-regulation of pulmonary pro-inflammatory and chemokine genes . In acute respiratory distress syndrome, pulmonary artery endothelial cells upregulate FXR to control lung endothelial permeability, and lung regeneration . Bile acids as FXR agonists were shown to reduce cell viability, increase intracellular reactive oxygen species (ROS) production, induce epithelial-mesenchymal transition (EMT), enhance migration and differentiation of lung fibroblasts to myofibroblasts leading to pulmonary fibrosis . Other reports showed that activation of FXR could suppress collagen deposition and TGF-β1 and SNAI1 expression in bleomycin-induced lung fibrosis . Interestingly, our results showed that SNAI1 was downregulated in SARS-COV-2-infected cells confirming the counteracting effect in FXR-SNAI axis. FXR plays a significant and finely tuned role in controlling the levels of its agonist bile acids to exert type I interferon antiviral response and prevent the toxic effect on immune cells viral elimination . On the contrary, FXR inhibits endoplasmic reticulum stress-induced NLRP3 inflammasome to prevent injury . This outcome is an advantage for the virus as viral replication is controlled by NLRP3 inflammasome-dependent antiviral immune responses. This indicates that SARS-COV-2 may evade the immune system by targeting the NLRP3 inflammasome through upregulation of FXR . In the context of lung cancer, NR1H4 gene alteration is a risk factor for LUSC in the Han Chinese population . It is also found to be a novel proto-oncogene in non-small cell lung cancer (NSCLC) as it promotes tumor growth . Its expression in NSCLC showed an inverse correlation between PD-L1 expression indicating an immunosuppressive role that might modulate responsiveness to anti-PD-1 immunotherapy .
IL2RA and IL2RB polymorphisms were associated with lung cancer risk in the Chinese Han population . Mutations in IL2RB cause severe immune dysregulation, specifically impaired immunity to viral infections  and multisystem autoimmunity by an expansion of nonfunctional T and NK cells . During viral infections, IL2Rβ can signal terminal exhaustion and suppress immune cell memory . IL2RB is selectively regulated during the early priming toward the Th17 cell phenotype . In bronchial epithelium, IL2RB expression is positively correlated with IFNG, Th17 biomarker IL17A, and IL10 expression . Based on that, we can speculate the viral-induced IL2RB can induce an augmented inflammation and add to the already damaged tissue a more damaging effect of deranged immune reaction, especially in patients with lung cancer.
Interferon-stimulated gene factor 15 (ISG15) is a 17-kDa protein encoded by ISG15 and has been implicated in the host antiviral response as one of the most strongly and rapidly induced responses. ISG15 exists in many forms whether ubiquitous or inducible, conjugated or unconjugated. Recent evidence suggests that the unconjugated form functions as a cytokine which can regulate viral replication and host responses. In a study on Kaposi’s sarcoma-associated herpesvirus, the causative agent of Karposi’s sarcoma which is the most common cancer associated with HIV/AIDS, the transcriptional analysis revealed ISG15 as one of the most induced genes. Interestingly, knock-down of this gene in the KSHV-infected primary oral fibroblasts resulted in increased virion release and expression of viral lytic genes. These results suggest that ISG15 is involved in the maintenance of latency . Other studies have suggested that elevated levels of ISG15 in tumor cells leads to decreased protein polyubiquitination and turnover in tumor cells. These results suggest that ISG15 may contribute to the deregulation of the ubiquitin/26S proteasome pathway .
Another gene identified in our analysis was EBI3 which was upregulated in A549-infected cells. This gene belongs to the IL-12 cytokine gene family. The product of EBI3 is IL-27 subunit B which has been shown to regulate T cell differentiation and suppression of angiogenesis . In fact, EBI3 has been identified as a serum and tissue marker of lung cancer . On the contrary, CLEC4e is part of the C-type lectin family of immune receptors and its expression is strongly upregulated by inflammatory stimuli. CLEC4E which is also known as Macrophage-inducible C-type lectin (MINCLE) has been shown to upregulate IL-1β expression but also may have anti-inflammatory properties by inducing the expression of IL-10. This dual role is dependent on ligand . However, in the context of cancer, it has been shown to have pro-tumorigenic properties in mouse and human pancreatic ductal adenocarcinoma .
IL11, another gene identified in our analysis, belongs to the IL-6 family of cytokines and is known to stimulate megakaryocytopoiesis with vital roles in inflammatory disease and cancers . Upregulation of IL11 in response to TGFβ1 exposure in fibroblasts stimulates fibrogenic protein synthesis which leads to fibrosis in response to injury . Increased expression of IL11R in lung tumors compared with adjacent non-malignant cells was recently documented, indicating a privileged use of cancer to the IL11 signaling pathways. This makes it a possible target to inhibit cancer . Targeting the IL11R in metastatic cancer has promising results . Interestingly, our data indicate that IL11 is associated with poor prognosis in lung cancer patients. Little is known on the function of IL11 in NSCLC. Recent studies have shown that IL11 is upregulated in NSCLC samples compared to normal tissue. This correlated with a poor prognosis. In vitro and in vivo experiments showed a role of IL11 in cell migration, proliferation and tumorigenesis .
Interestingly, recent reports showed an upregulation of IL-11 in human lungs in association with viral infections and other fibroinflammatory diseases. Moreover, other members of the IL-6 family was found to be involved in the cytokine storm of corona virus disease 2019 (COVID-19) , which is found to participate in the severity of COVID-19 illness.
In summary, the analysis showed that LUAD contain higher immune cells infiltrate compared to LUSC. The transcriptomic analysis of the in vitro data using A549 response to SARS-CoV-2 showed that cytokine-mediated signaling pathway (GO:0019221) was the most significant. Within that pathway IL11 was found to be most differentially expressed (P < 0.001). Further systems immunology analysis showed that IL11R is abundant in CD4 T cells and IL11 expression is correlated positively with mast cells, activated macrophages M1, and T follicular helper cells (Tfh).
| Conclusion|| |
Taken together, we can conclude that COVID-19 infection in NSCLC patients lead to the activation of specific cytokines. Our analysis also showed IL11 to be the most differentially expressed between the cancer and non-cancer patients suggesting that COVID-19 infection in cancer patients leads to the synergistic increase in expression of CD4+, M1, and Tfh cells. More studies to understand their potential use as biomarkers of disease severity and progression in COVID-19 patients with lung cancer is warranted.
The main limitation of our study is the fact that our approach was in silico approach. For that reason, further validation should be done to confirm the clinical value of our findings
SAH, MH, IH, RH designed experiments, analyzed the samples, and contributed to data interpretation and manuscript preparation. QH, AA, and AS contributed to the manuscript preparation and revision. All authors read and approved final version of the manuscript.
This work is exempted from Ethical approval since we used in silico approach without involvement of human or animal subjects.
Financial support and sponsorship
The work did not require funding grant.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data availability statement
All the original contributions presented in this manuscript are included in the article.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]