• Users Online: 83
  • Print this page
  • Email this page
  • Email this page
  • Email this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 1  |  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


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 Submission17-Feb-2022
Date of Decision04-Apr-2022
Date of Acceptance05-Apr-2022
Date of Web Publication29-Apr-2022

Correspondence Address:
Qutayba Hamid
Sharjah Institute for Medical Research, College of Medicine, University of Sharjah, Sharjah
Canada
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/abhs.abhs_12_22

Rights and Permissions
  Abstract 

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 Dec 3];1:79-91. Available from: http://www.abhsjournal.net/text.asp?2022/1/2/79/344318




  Background Top


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 [1]. 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 [2]. 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 [3]. Although the general population had only a 2.3% fatality rate, this percentage increased to 5.6% in cancer patients [4]. 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 [5]. NSCLC represents the majority of lung cancer, and it includes the most common subtypes: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) [6]. 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 [7]. 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 [8]. 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 [3]. 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 [8]. 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 Top


Datasets

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.

Click here to view


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 [13]. The DEGs from the two comparisons, healthy (infected and noninfected groups) versus cancer cells (infected and noninfected groups), were intersected.

Gene ontology

The resultant genes were explored for any common pathways using Metascape online tool for gene ontology (http://metascape.org) [14] and validated using classical statistics applied to the raw data.

Validation

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 [15] using TCGA_LUAD_2016 dataset [16]. 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/) [17].


  Results Top


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).

Click here to view


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.

Click here to view


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.

Click here to view
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.

Click here to view


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.

Click here to view


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.

Click here to view
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.

Click here to view


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.

Click here to view
Table 4: Differentially expressed genes in lung of infected mice.

Click here to view


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 [15] using TCGA_LUAD_2016 dataset [16], 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.

Click here to view


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.

Click here to view


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 [16] 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].
Table 5: Correlation between IL11 expression and immune cells.

Click here to view


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.

Click here to view



  Discussion Top


Reports from China showed that COVID-19 infection has a substantial consequence in patients with cancer receiving anti-tumor treatments [18]. 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 [8]. 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 [19]. 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 [20]. 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 [21]. 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 [22]. Although, IL-6 showed anti-apoptotic cancer inhibitory effects, it can promote cancer development which is opposite to the action of virus induced interferons [23]. By potentiating virus-induced apoptosis, interferons are considered anti-oncogenic [24].

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 [25]. NR1H4/FXR, when activated, actively suppresses autophagic gene promoters to regulate autophagy in response to nutrient status [26]. NR1H4 has a significant role in regulating the autophagy-ciliogenesis axis [27], which functions as sensory and signaling organelles [28]. 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 [29], 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 [30]. In acute respiratory distress syndrome, pulmonary artery endothelial cells upregulate FXR to control lung endothelial permeability, and lung regeneration [31]. 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 [32]. Other reports showed that activation of FXR could suppress collagen deposition and TGF-β1 and SNAI1 expression in bleomycin-induced lung fibrosis [33]. 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 [34]. On the contrary, FXR inhibits endoplasmic reticulum stress-induced NLRP3 inflammasome to prevent injury [35]. 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 [36]. In the context of lung cancer, NR1H4 gene alteration is a risk factor for LUSC in the Han Chinese population [37]. It is also found to be a novel proto-oncogene in non-small cell lung cancer (NSCLC) as it promotes tumor growth [38]. 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 [39].

IL2RA and IL2RB polymorphisms were associated with lung cancer risk in the Chinese Han population [40]. Mutations in IL2RB cause severe immune dysregulation, specifically impaired immunity to viral infections [41] and multisystem autoimmunity by an expansion of nonfunctional T and NK cells [42]. During viral infections, IL2Rβ can signal terminal exhaustion and suppress immune cell memory [43]. IL2RB is selectively regulated during the early priming toward the Th17 cell phenotype [44]. In bronchial epithelium, IL2RB expression is positively correlated with IFNG, Th17 biomarker IL17A, and IL10 expression [45]. 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 [46]. 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 [47].

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 [48]. In fact, EBI3 has been identified as a serum and tissue marker of lung cancer [49]. 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 [50]. However, in the context of cancer, it has been shown to have pro-tumorigenic properties in mouse and human pancreatic ductal adenocarcinoma [51].

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 [52]. Upregulation of IL11 in response to TGFβ1 exposure in fibroblasts stimulates fibrogenic protein synthesis which leads to fibrosis in response to injury [53]. 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 [54]. Targeting the IL11R in metastatic cancer has promising results [55]. 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 [56].

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) [57], 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 Top


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.

Study limitations

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

Authors’ contributions

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.

Ethical statement

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.



 
  References Top

1.
Han Q, Zheng B, Daines L, Sheikh A. Long-term sequelae of COVID-19: A systematic review and meta-analysis of one-year follow-up studies on post-COVID symptoms. Pathogens 2022;11:269.  Back to cited text no. 1
    
2.
Guan W-J, LiangWH, ZhaoY, LiangHR, ChenZS, LiYM, et al. Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis. European Respiratory Journal 2020;55:2000547.  Back to cited text no. 2
    
3.
Liang W, Guan W, Chen R, Wang W, Li J, Xu K, et al. Cancer patients in SARS-cov-2 infection: A nationwide analysis in china. Lancet Oncol 2020;21:335-7.  Back to cited text no. 3
    
4.
Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA 2020;323:1239-42.  Back to cited text no. 4
    
5.
de Marinis F, Attili I, Morganti S, Stati V, Spitaleri G, Gianoncelli L, et al. Results of multilevel containment measures to better protect lung cancer patients from COVID-19: The IEO model. Front Oncol 2020;10:665.  Back to cited text no. 5
    
6.
Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature 2018;553:446-54.  Back to cited text no. 6
    
7.
Carbone DP, Gandara DR, Antonia SJ, Zielinski C, Paz-Ares L. Non-small-cell lung cancer: Role of the immune system and potential for immunotherapy. J Thorac Oncol 2015;10:974-84.  Back to cited text no. 7
    
8.
Tian S, HuW, NiuL, LiuH, XuH, XiaoSY. Pulmonary pathology of early-phase 2019 novel coronavirus (COVID-19) pneumonia in two patients with lung cancer. J Thorac Oncol 2020;15:700-4.  Back to cited text no. 8
    
9.
Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 2020;8:420-2.  Back to cited text no. 9
    
10.
Zhang H, ZhouP, WeiY, YueH, WangY, et al. Histopathologic changes and SARS-CoV-2 immunostaining in the lung of a patient with COVID-19. Ann Intern Med 2020;172:629-32.  Back to cited text no. 10
    
11.
Daamen AR, Bachali P, Owen KA, Kingsmore KM, Hubbard EL, Labonte AC, et al. Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway. Sci Rep 2021;11:7052.  Back to cited text no. 11
    
12.
Blanco-Melo D, Nilsson-PayantBE, LiuW-C, UhlS, HoaglandD, MøllerR, et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 2020;181:1036-45 e9.  Back to cited text no. 12
    
13.
Emig D, Salomonis N, Baumbach J, Lengauer T, Conklin BR, Albrecht M. AltAnalyze and domaingraph: Analyzing and visualizing exon expression data. Nucleic Acids Res 2010;38:W755-62.  Back to cited text no. 13
    
14.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10:1523.  Back to cited text no. 14
    
15.
Cai L, Lin S, Girard L, Zhou Y, Yang L, Ci B, et al. LCE: An open web portal to explore gene expression and clinical associations in lung cancer. Oncogene 2019;38:2551-64.  Back to cited text no. 15
    
16.
The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014;511:543-50.  Back to cited text no. 16
    
17.
Nagy Á, LánczkyA, MenyhártO, GyőrffyB. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep 2018;8:9227.  Back to cited text no. 17
    
18.
Zhang L, ZhuF, XieL, WangC, WangJ, ChenR. Clinical characteristics of COVID-19-infected cancer patients: A retrospective case study in three hospitals within Wuhan, China. Ann Oncol 2020;31:894-901.  Back to cited text no. 18
    
19.
Honda T, Ota H, Yamazaki Y, Yoshizawa A, Fujimoto K, Sone S. Proliferation of type II pneumocytes in the lung biopsy specimens reflecting alveolar damage. Respir Med 2003;97:80-5.  Back to cited text no. 19
    
20.
Qu J, YangR, SongL, KamelIR. Atypical lung feature on chest CT in a lung adenocarcinoma cancer patient infected with COVID-19. Ann Oncol 2020;31:825-6.  Back to cited text no. 20
    
21.
Elinav E, Nowarski R, Thaiss CA, Hu B, Jin C, Flavell RA. Inflammation-induced cancer: Crosstalk between tumours, immune cells and microorganisms. Nat Rev Cancer 2013;13:759-71.  Back to cited text no. 21
    
22.
Brichory FM, Misek DE, Yim AM, Krause MC, Giordano TJ, Beer DG, et al. An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer. Proc Natl Acad Sci U S A 2001;98:9824-9.  Back to cited text no. 22
    
23.
Philip M, Rowley DA, Schreiber H. Inflammation as a tumor promoter in cancer induction. Semin Cancer Biol 2004;14:433-9.  Back to cited text no. 23
    
24.
Balachandran S, Roberts PC, Kipperman T, Bhalla KN, Compans RW, Archer DR, et al. Alpha/beta interferons potentiate virus-induced apoptosis through activation of the FADD/caspase-8 death signaling pathway. J Virol 2000;74:1513-23.  Back to cited text no. 24
    
25.
Ma K, Saha PK, Chan L, Moore DD. Farnesoid X receptor is essential for normal glucose homeostasis. J Clin Invest 2006;116:1102-9.  Back to cited text no. 25
    
26.
Lee JM, Wagner M, Xiao R, Kim KH, Feng D, Lazar MA, et al. Nutrient-sensing nuclear receptors coordinate autophagy. Nature 2014;516: 112-5.  Back to cited text no. 26
    
27.
Liu ZQ, Lee JN, Son M, Lim JY, Dutta RK, Maharjan Y, et al. Ciliogenesis is reciprocally regulated by PPARA and NR1H4/FXR through controlling autophagy in vitro and in vivo. Autophagy 2018;14:1011-27.  Back to cited text no. 27
    
28.
Wrighton KH. Autophagy and ciliogenesis come together. Nature Reviews Molecular Cell Biology 2013;14:687-687.  Back to cited text no. 28
    
29.
Yaghi A, Dolovich MB. Airway epithelial cell cilia and obstructive lung disease. Cells 2016;5:40.  Back to cited text no. 29
    
30.
Fei J, Fu L, Hu B, Chen YH, Zhao H, Xu DX, et al. Obeticholic acid alleviate lipopolysaccharide-induced acute lung injury via its anti-inflammatory effects in mice. Int Immunopharmacol 2019;66:177-84.  Back to cited text no. 30
    
31.
Zhang L, Li T, Yu D, Forman BM, Huang W. FXR protects lung from lipopolysaccharide-induced acute injury. Mol Endocrinol 2012;26:27-36.  Back to cited text no. 31
    
32.
Chen B, Cai HR, Xue S, You WJ, Liu B, Jiang HD. Bile acids induce activation of alveolar epithelial cells and lung fibroblasts through farnesoid X receptor-dependent and independent pathways. Respirology 2016;21:1075-80.  Back to cited text no. 32
    
33.
Comeglio P, Filippi S, Sarchielli E, Morelli A, Cellai I, Corcetto F, et al. Anti-fibrotic effects of chronic treatment with the selective FXR agonist obeticholic acid in the bleomycin-induced rat model of pulmonary fibrosis. J Steroid Biochem Mol Biol 2017;168:26-37.  Back to cited text no. 33
    
34.
Honke N, Shaabani N, Hardt C, Krings C, Häussinger D, Lang PA, et al. Farnesoid X receptor in mice prevents severe liver immunopathology during lymphocytic choriomeningitis virus infection. Cell Physiol Biochem 2017;41:323-38.  Back to cited text no. 34
    
35.
Han CY, Rho HS, Kim A, Kim TH, Jang K, Jun DW, et al. FXR inhibits endoplasmic reticulum stress-induced NLRP3 inflammasome in hepatocytes and ameliorates liver injury. Cell Rep 2018;24:2985-99.  Back to cited text no. 35
    
36.
Zhao C, Zhao W. NLRP3 inflammasome—A key player in antiviral responses. Frontiers in Immunology 2020;11:211.  Back to cited text no. 36
    
37.
Dong J, Jin G, Wu C, Guo H, Zhou B, Lv J, et al. Genome-wide association study identifies a novel susceptibility locus at 12q23.1 for lung squamous cell carcinoma in han chinese. Plos Genet 2013;9:e1003190.  Back to cited text no. 37
    
38.
You W, Chen B, Liu X, Xue S, Qin H, Jiang H. Farnesoid X receptor, a novel proto-oncogene in non-small cell lung cancer, promotes tumor growth via directly transactivating CCND1. Sci Rep 2017;7:591.  Back to cited text no. 38
    
39.
You W, Li L, Sun D, Liu X, Xia Z, Xue S, et al. Farnesoid X receptor constructs an immunosuppressive microenvironment and sensitizes fxrhighpd-l1low NSCLC to anti-PD-1 immunotherapy. Cancer Immunol Res 2019;7:990-1000.  Back to cited text no. 39
    
40.
Jia Z, Zhang Z, Yang Q, Deng C, Li D, Ren L. Effect of IL2RA and IL2RB gene polymorphisms on lung cancer risk. Int Immunopharmacol 2019;74:105716.  Back to cited text no. 40
    
41.
Campbell TM, Bryceson YT. IL2RB maintains immune harmony. J Exp Med 2019;216:1231-3.  Back to cited text no. 41
    
42.
Fernandez IZ, Baxter RM, Garcia-Perez JE, Vendrame E, Ranganath T, Kong DS, et al. A novel human IL2RB mutation results in T and NK cell-driven immune dysregulation. J Exp Med 2019;216:1255-67.  Back to cited text no. 42
    
43.
Beltra JC, Bourbonnais S, Bédard N, Charpentier T, Boulangé M, Michaud E, et al. IL2RΒ-dependent signals drive terminal exhaustion and suppress memory development during chronic viral infection. Proc Natl Acad Sci U S A 2016;113:E5444-53.  Back to cited text no. 43
    
44.
Tuomela S, Salo V, Tripathi SK, Chen Z, Laurila K, Gupta B, et al. Identification of early gene expression changes during human th17 cell differentiation. Blood 2012;119:e151-60.  Back to cited text no. 44
    
45.
Li X, Hawkins GA, Moore WC, Hastie AT, Ampleford EJ, Milosevic J, et al. Expression of asthma susceptibility genes in bronchial epithelial cells and bronchial alveolar lavage in the severe asthma research program (SARP) cohort. J Asthma 2016;53:775-82.  Back to cited text no. 45
    
46.
Dai L, Bai L, Lin Z, Qiao J, Yang L, Flemington EK, et al. Transcriptomic analysis of KSHV-infected primary oral fibroblasts: The role of interferon-induced genes in the latency of oncogenic virus. Oncotarget 2016;7:47052-60.  Back to cited text no. 46
    
47.
Desai SD, Haas AL, Wood LM, Tsai YC, Pestka S, Rubin EH, et al. Elevated expression of ISG15 in tumor cells interferes with the ubiquitin/26S proteasome pathway. Cancer Res 2006;66:921-8.  Back to cited text no. 47
    
48.
Jiang J, Liu X. Upregulated EBI3 correlates with poor outcome and tumor progression in breast cancer. Oncol Res Treat 2018;41:111-5.  Back to cited text no. 48
    
49.
Nishino R, Takano A, Oshita H, Ishikawa N, Akiyama H, Ito H, et al. Identification of epstein-barr virus-induced gene 3 as a novel serum and tissue biomarker and a therapeutic target for lung cancer. Clin Cancer Res 2011;17:6272-86.  Back to cited text no. 49
    
50.
Chiffoleau E. C-type lectin-like receptors as emerging orchestrators of sterile inflammation represent potential therapeutic targets. Front Immunol 2018;9:227.  Back to cited text no. 50
    
51.
Seifert L, Werba G, Tiwari S, Giao Ly NN, Alothman S, Alqunaibit D, et al. The necrosome promotes pancreatic oncogenesis via CXCL1 and mincle-induced immune suppression. Nature 2016;532: 245-9.  Back to cited text no. 51
    
52.
Nguyen PM, Abdirahman SM, Putoczki TL. Emerging roles for interleukin-11 in disease. Growth Factors 2019;37:1-11.  Back to cited text no. 52
    
53.
Schafer S, Viswanathan S, Widjaja AA, Lim WW, Moreno-Moral A, DeLaughter DM, et al. IL-11 is a crucial determinant of cardiovascular fibrosis. Nature 2017;552:110-5.  Back to cited text no. 53
    
54.
Cardó-Vila M, Marchiò S, Sato M, Staquicini FI, Smith TL, Bronk JK, et al. Interleukin-11 receptor is a candidate target for ligand-directed therapy in lung cancer: Analysis of clinical samples and BMTP-11 preclinical activity. Am J Pathol 2016;186:2162-70.  Back to cited text no. 54
    
55.
Pasqualini R, Millikan RE, Christianson DR, Cardó-Vila M, Driessen WH, Giordano RJ, et al. Targeting the interleukin-11 receptor α in metastatic prostate cancer: A first-in-man study. Cancer 2015;121: 2411-21.  Back to cited text no. 55
    
56.
Zhao M, Liu Y, Liu R, Qi J, Hou Y, Chang J, et al. Upregulation of IL-11, an IL-6 family cytokine, promotes tumor progression and correlates with poor prognosis in non-small cell lung cancer. Cell Physiol Biochem 2018;45:2213-24.  Back to cited text no. 56
    
57.
Hirano T. IL-6 in inflammation, autoimmunity and cancer. Int Immunol 2021;33:127-48.  Back to cited text no. 57
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Background
Materials and me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed723    
    Printed114    
    Emailed2    
    PDF Downloaded71    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]