Abstract

Background

   SARS-CoV-2 and other viruses rely on the protease function of the
   TMPRSS2 protein to invade host cells. Despite cancer patients often
   experience poorer outcomes following SARS-CoV-2 infection, the role of
   TMPRSS2 in different cancer types has not yet been analyzed in detail.
   Therefore, the aim of the study was to determine the expression,
   function and clinical relevance of TMPRSS2 in tumors.

Methods

   Publicly accessible RNA sequencing data from tumors, adjacent tissues
   and whole blood samples of COVID-19 patients as well as data from human
   tumor epithelial and endothelial cells infected with SARS-CoV-2 were
   analyzed for TMPRSS2 expression and correlated to the expression of
   immune-relevant genes and clinical parameters. In vitro models of cells
   transfected with TMPRSS2 (TMPRSS2^high), siTMPRSS2 or mock controls
   (TMPRSS2^low cells) were analyzed by qPCR, flow cytometry, ELISA and
   Western blot for the expression of immune response-relevant molecules.
   Co-cultures of TMPRSS2 model systems with blood peripheral mononuclear
   cells were employed to evaluate immune cell migration, cytotoxicity and
   cytokine release.

Results

   Higher expression levels of TMPRSS2 were found in blood from patients
   infected with SARS-CoV-2, while TMPRSS2 expression levels significantly
   varied between the tumor types analyzed. TMPRSS2^high tumor cells
   exhibit increased activity of the interferon (IFN) signal pathway
   accompanied by an increased expression of class I human leukocyte
   antigens (HLA-I) and programmed cell death ligand 1 (PD-L1) elevated
   interleukin 6 (IL-6) secretion and reduced NK cell-mediated
   cytotoxicity compared to TMPRSS2^low mock controls. Treatment with a
   Janus kinase (JAK) 2 inhibitor or TMPRSS2-specific siRNA decreased
   TMPRSS2 expression. Co-cultures of the in vitro TMPRSS2 models with
   peripheral blood mononuclear cells in the presence of the immune
   checkpoint inhibitor nivolumab resulted in a significantly increased
   migration and infiltration of immune cells towards TMPRSS2^high cells
   and a reduced release of the innate immunity-related cytokines CCL2 and
   CCL3.

Conclusions

   This study provides novel insights into the role of TMPRSS2 in various
   tumor systems and the impact of SARS-CoV-2 infection on the host
   immunogenicity via the activation of immune-relevant pathways. These
   findings were linked to the efficacy of immune checkpoint inhibitor
   therapy, offering a potential alternative strategy to mitigate the
   severity of COVID-19.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12967-025-06177-z.

   Keywords: SARS-CoV-2, TMPRSS2, Immune escape, Immune response, Tumors

Background

   Coronavirus disease 2019 (COVID-19) has rapidly emerged as a global
   health pandemic mediated by infection with the severe acute respiratory
   syndrome coronavirus 2 (SARS-CoV-2). The virus utilizes the
   angiotensin-converting enzyme 2 (ACE2) receptor for its cellular entry
   and the transmembrane protease serine 2 (TMPRSS2) for the priming of
   the spike (S) protein [[44]1]. Since some coronaviruses, such as
   SARS-CoV-1, MERS-CoV, and SARS-CoV-2, are activated by TMPRSS2 [[45]2],
   there is an urgent need for the development of broad anti-viral
   strategies targeting multiple coronaviruses by focusing on common
   elements, such as TMPRSS2 and ACE2. In this context, TMPRSS2 inhibitors
   have been shown to block viral infections [[46]3] suggesting their use
   as a potential therapeutic approach [[47]4, [48]5].

   Moreover, there exists growing evidence that tumor patients have an
   increased risk of COVID-19 infection and are more susceptible to severe
   illness with a worse outcome [[49]6–[50]12]. So far, there exists only
   limited information about the link between the expression of
   immune-relevant molecules in cancer patients, susceptibility to
   SARS-CoV-2 infection and the severity of the disease [[51]13]. For
   example, an upregulation of the programmed cell death ligand (PD-L)1
   expression during the cytokine storm of SARS-CoV-2-infection has been
   detected [[52]14], but also infections with other viruses have been
   shown to upregulate PD-L1 expression, leading to immune escape [[53]14,
   [54]15]. Consequently, PD-L1/PD-1 immune checkpoint inhibitor (ICPi)
   therapy might represent a potential treatment option to enable T cells
   to combat cancer cells [[55]16], while controlling SARS-CoV-2
   infection.

   Under physiological conditions, TMPRSS2 is expressed in various
   tissues, including pancreas, prostate, stomach, kidney, small intestine
   as well as the respiratory tract, which is a primary site of SARS-CoV-2
   infection [[56]17]. The TMPRSS2 expression in pan-tissues is associated
   with pathways involved in immune metabolism, cell growth as well as
   stromal and cancer signatures [[57]18]. During pathophysiological
   conditions, the TMPRSS2 expression levels and their prognostic value
   highly varied across different cancer types [[58]17]. For example,
   TMPRSS2 levels are elevated in prostate cancer [[59]19], but decreased
   in colon cancer when compared to corresponding normal tissues [[60]17].
   Furthermore, the expression of TMPRSS2 in lung adenocarcinoma is linked
   to a positive prognosis, while it is associated with a negative
   prognosis in breast cancer [[61]20].

   Cancer patients may have a higher mortality rate due to a
   COVID-19-mediated hyperinflammatory state, which is accompanied by
   severe disease manifestations [[62]8, [63]14]. In severe cases, virus
   spread is linked to excessive IL-6 production associated with severe
   inflammation and tissue damage, while this response is controlled in
   mild cases [[64]14, [65]21]. In contrast, IL-2 has the potential to
   improve the immune response to COVID-19 but is also a key factor in
   disease severity due to its involvement in cytokine release and immune
   regulation [[66]22].

   The JAK-STAT pathway is a fundamental signaling mechanism involved in
   anti-viral and anti-tumoral immune responses [[67]23], but also
   involved in the hyperinflammatory response occuring in severe COVID-19
   cases [[68]24]. However, the impact of SARS-CoV-2-associated molecules,
   including TMPRSS2, on the JAK-STAT signal transduction has not yet been
   determined. Therefore, an increased understanding of the functions and
   interactions of TMPRSS2 with the JAK-STAT pathway and their regulated
   molecules, such as class I human leukocyte antigens (HLA-I) and PD-L1,
   is urgently needed and might provide insights into their potential as
   therapeutic targets thereby enhancing the patients’ outcomes by
   improving the management of COVID-19.

   Consequently, this study aims to elucidate the expression patterns and
   clinical significance of TMPRSS2 across different cancer types by
   analyzing publicly available RNA sequencing data, to assess in vivo
   immunological pathways associated with varying TMPRSS2 expression
   levels and to explore how their dynamics might affect the outcome of
   SARS-CoV-2-infected cancer patients. These results will provide
   insights into potential therapeutic approaches, including immune
   checkpoint inhibitors (ICPi), to improve the management of cancer
   patients during emerging and re-emerging coronavirus outbreaks.

Methods

Cell culture and transfections

   The human tumor epithelial cell lines MCF-7 (breast cancer (BC)),
   MDA-MB-468 (BC) and the endothelial cell line EA.Hy926 were obtained
   from the American Type Culture Collection (ATCC, Manassas, USA). All
   cell lines were grown in RPMI1640 medium with the exception of
   EA.Hy926, which was cultured in DMEM/F12. The culture mediums were
   supplemented with 2 mM L-glutamine, 10% fetal calf serum (FCS,
   PAN-Biotech, Aidenbach, Germany) and appropriate antibiotics.

   Cell lines were transfected with the TMPRSS2 expression vector,
   designated as TMPRSS2^high (Addgene, Watertown, USA), using the
   Effectene Transfection Reagent (Qiagen, Hilden, Germany) according to
   the manufacturer’s instructions. A control was established using a mock
   vector, referred to as TMPRSS2^low/mock.

   Lipid nanoparticles (LNPs) were formulated by mixing lipid stock
   solutions in ethanol, composed of D-Lin-MC3-DMA, DPPC, Cholesterol, and
   PEG-DMG in a molar ratio of 50:10:35:5. These were combined with either
   a siTMPRSS2 or negative control (Thermo Fisher, Waltham, USA),
   utilizing an amine-to-phosphate (N/P) ratio of 3.1, which corresponds
   to 0.054 µg of nucleic acid per µmol of total lipid. Subsequently, the
   LNP complex was used to transfect the TMPRSS2^high MDA-MB-468 cells,
   resulting in the generation of TMPRSS2-silenced cells.

RNA extraction and real-time quantitative RT-PCR

   Cellular RNA was extracted using the NucleoSpin RNA II kit
   (Macherey-Nagel, Düren, Germany). 2 µg RNA/ sample was converted into
   cDNA using the Revert H Minus First Strand cDNA synthesis kit
   (Fermentas, Thermo Fisher) and oligo(dT)18 primer following the
   manufacturer’s instructions. Utilizing a standard laboratory protocol,
   qRT-PCR was conducted on a Bio-Rad CFX96 system (Bio-Rad, Hercules,
   USA) with the platinum SYBRGreen qPCR Supermix UDG (Thermo Fisher) as
   described [[69]25] using primers directed against HLA-I, APM
   components, IFN signaling molecules, PD-L1 and TMPRSS2-regulated genes
   (Supplementary Table [70]1). Data were evaluated using the comparative
   quantification mode of the CFX Maestro Software 2.3 (Bio-Rad) and
   presented as a mean of a minimum of three independent experiments.

Western blot analysis

   For Western blot analysis, 30 µg protein/ sample was separated by
   SDS-PAGE and then transferred using the iBlot™ 2 Dry Blotting System
   (Invitrogen, Thermo Fisher) prior to staining with antibodies (Abs)
   specific to TMPRSS2, IFI27 and STAT1 (Cell Signaling Technology,
   Danvers, USA). To ensure equal protein loading, the blot was stained
   with a monoclonal antibody (mAb) against ACTB (Cell Signaling
   Technology, Danvers, USA). A horse-reddish peroxidase (HRP) conjugated
   secondary Ab was applied before visualizing the proteins using an
   ECL-based system for chemiluminescence.

Flow cytometry

   Flow cytometry was conducted as recently described [[71]26]. The
   antibodies employed for flow cytometry were HLA-I and PD-L1 mAbs
   (Invitrogen, Waltham, USA). Briefly, tumor cells were labeled with the
   appropriate amounts of antibodies at 4 °C in darkness for 30 min. HLA-I
   and PD-L1 expression was assessed on a NAVIOS flow cytometer (Beckman
   Coulter, Brea, USA) and data was analyzed using the Kaluza Software.
   The results were presented as mean fluorescence intensity (MFI) from at
   least three independent experiments.

Quantification of IL-6 by ELISA

   To quantify IL-6 in TMPRSS2^low and TMPRSS2^high EA.Hy926 cells, the
   supernatants were collected, and the amount of IL-6 produced by the
   endothelial cells was calculated using an ELISA (LEGENDplex™
   Macrophage/Microglia Panel, BioLegend) following the manufacturer’s
   instructions. Data acquisition was conducted using the BD Symphony A3,
   and analysis was performed with the LEGENDplex™ Data Analysis Software
   Suite.

mRNA sequencing and data analyses

   RNA sequencing was performed on the Illumina Noveseq 6000 platform by
   Novogene (Cambridge, United Kingdom). The reference genome and gene
   model annotation files were obtained from the genome website
   (NCBI/UCSC/Ensembl). HTSeq v0.6.1 was used to quantify gene expression
   levels, and the FPKM of each gene was calculated based on the gene’s
   length and the mapped read counts [[72]27]. The initial analysis of
   differential gene expression (DGE) between TMPRSS2^low and TMPRSS2^high
   MCF7 and EA.Hy926 cells using the DESeq2 R package (2_1.6.3) was
   conducted by Novogene (Munich, Germany). The p-values were adjusted
   using Benjamini and Hochberg’s approach to control the False Discovery
   Rate (FDR). Genes with an adjusted p-value (Padj) < 0.05 in DESeq2 data
   were identified as differentially expressed. The differentially
   expressed genes (DEGs) were subjected to Gene Ontology (GO) and Kyoto
   Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the
   clusterProfiler (v4.0.5) R package. KEGG terms with corrected Padj
   value < 0.05 were considered significantly enriched.

Microfluidic chip assay

   TMPRSS2^high and TMPRSS2^low MCF-7 cells were labeled with CellTrace™
   Far Red (Invitrogen, Thermo Fisher) according to the manufacturer’s
   instructions for microfluidic chip tests and then suspended in
   RPMI1640-based myogel/fibrin gel derived from human tumors containing
   2.4 mg/ml myogel, 0.5 mg/ml fibrinogen (Merck, Darmstadt, Germany),
   33.3 µg/ml aprotinin (Sigma-Aldrich) and 0.3 U/ml thrombin
   (Sigma-Aldrich) as well as 5 µM of IncuCyte caspase-3/7 green
   (Sartorius, Göttingen, Germany) to identify apoptotic cells. The
   TMPRSS2^high and TMPRSS2^low MCF-7 cells were then divided into a
   control group without drug and a group treated with 0.5 µM nivolumab
   (Opvido^®, Selleckchem, Houston, Texas, USA). Subsequently, 2 µL of
   cell suspension (i.e., 500 cells) were loaded into separate small
   “cancer cell channels” of the microfluidic chip as previously described
   [[73]28].

   Peripheral blood mononuclear cells (PBMNCs) purified by gradient
   density centrifugation from the buffy coats of healthy donors provided
   by the Finnish Red Cross were stained with CellTrace™ Violet
   (Invitrogen) following the manufacturer’s instructions. Cell viability
   and number were assessed using trypan blue staining with CellCountess
   (Invitrogen). After staining, the cells were suspended in cell culture
   media supplemented with 10 ng/ml of recombinant human IL-2 (BioLegend,
   San Diego, California, USA) and 5 µM caspase-3/7 green (Sartorius). The
   PBMNCs were then divided into a control group without drug and a group
   treated with 0.5 µM nivolumab. Subsequently, 1 × 10^5 viable PBMNCs in
   in 100 µL was added to the larger ‘PBMNC channels’ of the chip as
   previously described [[74]28]. In controls without PBMNCs, 100 µL of
   cell culture media containing 5 µM caspase-3/7 green was injected.

   Following the injections, the chips were placed in a cell culture
   incubator and incubated for 72 h with daily imaging conducted using a
   Nikon Ti-E with Alveole Primo microscope (Nikon, Tokyo, Japan)
   connected to a Hamamatsu Orca Flash 4.0 LT B&W camera (Hamamatsu
   Photonics, Hamamatsu, Japan). The conditioned medium was then collected
   from the chips and stored at -80 °C until further use.

Cytokine profiling

   The Abcam FirePlex Service (Boston, USA) was used to perform cytokine
   profiling on the conditioned media from the microfluidic chips with a
   FirePlex^®-96 Key Cytokines (Human) Immunoassay Panel (Abcam,
   Cambridge, UK). This panel can detect 17 cytokines, including the
   granulocyte-macrophage colony-stimulating factor (CSF2, GM-CSF), IL-1B,
   IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 A, IL-13 and IL-17 A, CXCL8,
   IFNγ, monocyte chemoattractant protein-1 (MCP-1, CCL2), macrophage
   inflammatory protein 1 alpha (MIP1-α, CCL3), macrophage inflammatory
   protein 1 beta (MIP1-β, CCL4) and tumor necrosis factor (TNF)-α. Each
   sample was analyzed in duplicate.

NK cell activity

   Human PBMNCs were activated for 18 h using 1 ng/ml of IL-12, 5 ng/ml of
   IL-15 (Immunotools, Friesoythe, Germany) and 50 ng/ml of IL-18
   (Biovision, Milpitas, CA, USA) in X–vivo15 medium (Lonza).
   Subsequently, the cells were co-cultured with TMPRSS2^high and
   TMPRSS2^low target cells at a ratio of 1:1 followed by degranulation
   assay. Briefly, an anti-CD107a antibody was added for 1 h to the
   co-culture before the cells were stained with monoclonal antibodies
   (mAbs) against CD3, CD16 and CD56 (BioLegend) after 4 h to identify NK
   cells and assess overall NK cell activity.

Multi-dataset analysis of transcriptomic and genomic data

   Transcriptomic data from 24 healthy controls and 62 COVID-19 patients
   [[75]29, [76]30] were examined using COVID19db (ID: COVID000010).
   Previously published characteristics of the patients, such as age,
   gender and blood parameters, were referenced [[77]29]. Data from Calu3
   cells, a lung adenocarcinoma cell line infected with SARS-CoV-2, was
   obtained from GEO: [78]GSE147507. Metadata from a cohort of SARS-CoV-2
   and other respiratory viruses, including human parainfluenza virus 3
   (HPIV3), respiratory syncytial virus (RSV), mutant influenza A virus
   (IAVdNS) infected cells as well as COVID-19 positive lung biopsies (GEO
   accession: [79]GSE147507) were analyzed using ImmGen of Immunological
   Genome Project [[80]31–[81]33]. Single-cell RNA-seq data of peripheral
   blood from patients with severe COVID-19 were obtained from ImmGen
   [[82]34]. Additional datasets were obtained from the Gene Expression
   Omnibus (GEO), which included the following: an organoid model infected
   with the SARS-CoV-2 Alpha variant (B.1.1.7) (GEO: [83]GSE178333),
   samples from patients experiencing long COVID-19-a condition marked by
   persistent symptoms following SARS-CoV-2 infection (GEO: [84]GSE224615;
   [[85]35]) and samples from C57BL/6J wild-type mice infected with
   SARS-CoV-2 (GEO: [86]GSE253635; [[87]36]). The expression of the
   TMPRSS2 transcript across different tumor types and their corresponding
   normal tissues was analyzed using The Cancer Genome Atlas (TCGA)
   datasets through UALCAN [[88]37, [89]38]. Metadata from a cohort of BC
   (n = 782) and lung cancer (n = 91) cases obtained from TCGA (portal:
   [90]https://portal.gdc.cancer.gov) [[91]39] and GEO dataset
   ([92]GSE18842) were analyzed using the R2: Genomics analysis and
   visualization platform ([93]http://r2.amc.nl). The gene expression
   patterns of TMPRSS2, HLA class I APM, IFN pathway components and PD-L1
   were extracted from the datasets.

Statistical analysis

   The graphical presentations, Student’s t-test and one-way ANOVA were
   conducted using Microsoft Excel-Office 365, BioRender and R (RStudio
   3.0). Analysis of cytokine release was performed using the FirePlex™
   Analysis Workbench software
   ([94]https://www.abcam.com/kits/fireplex-analysis-workbench-software).
   A p-value < 0.05 was considered statistically significant (*, p < 0.05;
   **, p < 0.01; ***, p < 0.001).

Results

High levels of TMPRSS2 expression following SARS-CoV-2 infection

   RNAseq data from PBMNCs collected from 24 healthy individuals and 62
   COVID-19 patients (COVID19db ID: COVID000010) revealed increased levels
   of TMPRSS2 mRNA in PBMNCs after SARS-CoV-2 infection (Fig. [95]1A).
   This increase was comparable to the TMPRSS2 mRNA levels observed in
   SARS-CoV-2-infected Calu3 lung carcinoma cells (Fig. [96]1B). Analyses
   of TCGA datasets showed a heterogeneous expression of TMPRSS2 across
   different cancer types. When compared to corresponding normal tissue,
   higher mRNA expression levels of TMPRSS2 were found in BLCA, CESE,
   KICH, PRAD and UCEC, lower in BRCA, COAD, HNSC, KIRC, KIRP, LIHC, LUAD,
   LUSC and READ whereas TMPRSS2 expression was unchanged in CHOL, ESCA,
   GBM, PAAD, PCPG, SARC, SKCM, THCA, THYM and STAD (Fig. [97]1C).

Fig. 1.

   [98]Fig. 1
   [99]Open in a new tab

   Increased levels of TMPRSS2 expression in blood samples from COVID-19
   patients and in cell lines infected with SARS-CoV-2 compared to
   uninfected controls. A: TMPRSS2 expression levels were analyzed in
   blood samples from 24 healthy individuals and 62 patients with
   COVID-19. These data were obtained from whole blood transcriptomics
   analyses (COVID19db). B: TMPRSS2 expression in Calu3 lung carcinoma
   epithelial cells was evaluated after infection with SARS-CoV-2 (GEO:
   [100]GSE147507). C: The TMPRSS2 transcriptomic expression across
   various tumor types (red bars) and their adjacent normal tissues (blue
   bars) was examined using TCGA datasets. The results are displayed in a
   bar chart. t.test, ns: p > 0.05; *: p ≤ 0.05; **: p ≤ 0.01; ***:
   p ≤ 0.001

Influence of TMPRSS2 overexpression on transcriptomic profiles

   To determine the effect of TMPRSS2 on the gene expression pattern,
   MCF-7 and EA.Hy926 were transfected with a TMPRSS2 expression vector.
   Global transcriptomic profiles obtained after RNA sequencing of
   TMPRSS2^high vs. TMPRSS2^low MCF-7 cells and EA.Hy926 demonstrated 4452
   and 1949 DEGs in MCF-7 and EA.Hy926 cells, respectively. Among these
   DEGs, 2061 genes were upregulated and 2391 genes were downregulated in
   TMPRSS2^high MCF-7 (Supplementary Fig. [101]1A), while 1108 genes were
   upregulated and 841 genes were downregulated in TMPRSS2^high EA.Hy926
   (Supplementary Fig. [102]1B). Analyses of the top 30 upregulated and
   downregulated genes revealed 11 commonly upregulated genes (IFI6,
   TMPRSS2, IFIT1, IFIT2, IFIT3, OAS2, OASL, HLA-B, ISG15, LGALS3BP and
   IFI44L) and only one commonly downregulated gene (PRR11) in the two
   cell lines. The commonly upregulated genes of the in vitro models were
   also upregulated in COVID-19 vs. healthy PBMNCs, as highlighted in the
   Volcano plot (Fig. [103]2A). The KEGG terms retrieved from COVID19db
   (Fig. [104]2B) and the DEGs in TMPRSS2^high MCF-7 (Fig. [105]2C) and
   EA.Hy926 (Fig. [106]2D) showed that Epstein-Barr virus infection and
   the NOD-like receptor signaling pathway were similarly enriched across
   all three settings: TMPRSS2^high MCF-7, TMPRSS2^high EA.Hy926, and
   COVID-19 PBMNCs. In addition, within these KEGG terms, there were 12
   commonly upregulated genes, namely OAS2, OAS1, OAS3, STAT1, TNFAIP3,
   IRF7, STAT2, NFKBIA, IFNB1, MYD88, IRF9 and IL-6. Correlation plots
   between TMPRSS2 and commonly upregulated genes were representively
   shown for OAS1, STAT1 and IRF7 from COVID-19 vs. healthy PBMNCs
   (Fig. [107]2E). In contrast, no common KEGG terms were found in
   downregulated DEGs between TMPRSS2 transfectants of MCF-7 and EA.Hy926
   and PBMNCs of COVID-19 patients (Supplementary Fig. [108]2). To expand
   the analysis, the upregulated genes identified in our cell culture
   system (MCF-7 and EA.Hy926 cells) were further investigated in an
   organoid model infected with the SARS-CoV-2 alpha variant (B.1.1.7)
   (GEO: [109]GSE178333) and in patients with long COVID-19, a condition
   characterized by post-acute sequelae following SARS-CoV-2 infection
   (GEO: [110]GSE224615; [[111]35]). As shown in Supplementary Table
   [112]2, genes associated with the IFN pathway, such as IFI27, IFIT1,
   IFIT2, OASL and OAS3, showed significantly higher expression levels, in
   particular individuals with long COVID-19 when compared to non-long
   COVID-19 and the organoid systems infected with the alpha variant.

Fig. 2.

   [113]Fig. 2
   [114]Open in a new tab

   An analysis of transcriptional profiles and KEGG pathway enrichments in
   COVID-19 vs. healthy PBMNCs along with TMPRSS2^high versus TMPRSS2^low
   MCF-7 and EA.Hy926 cells were conducted using RNA sequencing. A: A
   Volcano plot depicts the differentially expressed genes (DEGs) between
   COVID-19 and healthy blood samples (COVID19db). Genes that are
   significantly downregulated are indicated in dark blue, while
   significantly upregulated genes are shown in red and non-significantly
   regulated genes are represented in grey. B: The top 10 enriched KEGG
   pathways from the upregulated genes between COVID-19 and healthy blood
   samples are presented. C, D: The top 20 enriched KEGG terms derived
   from the upregulated genes of TMPRSS2^high versus TMPRSS2^low MCF-7 (C)
   and EA.Hy926 (D) cells are displayed. The enriched GO terms found in
   both TMPRSS2^high and COVID-19 are highlighted with green circles. E:
   Correlation plots illustrate the commonly upregulated genes in the two
   most enriched KEGG pathways concerning TMPRSS2 expression from blood
   samples of COVID-19 patients (COVID19db). The gene expressions of OAS1,
   STAT1 and IRF7 are exemplified here

Impact of TMPRSS2 on HLA-I expression, IFN signaling and NK cell recognition

   Since the RNAseq data revealed an increased expression of IFN signaling
   genes in TMPRSS2^high vs. TMPRSS2^low cells, the impact of TMPRSS2 on
   the expression of HLA-I surface antigens was determined in MCF-7 and
   EA.Hy926 cells by flow cytometry. As shown in Fig. [115]3A,
   overexpression of TMPRSS2 resulted in an upregulation of HLA-I surface
   antigens, which could be correlated to enhanced mRNA expression of
   major components of the antigen processing machinery (APM) such as the
   transporter associated with antigen processing (TAP)1, TAP2 and TAPBP
   were upregulated by TMPRSS2 overexpression (Fig. [116]3B). As expected,
   the TMPRSS2-mediated HLA-I upregulation had also an impact on NK cell
   responses. Using a CD107 degranulation assay, a reduced NK cell
   recognition of TMPRSS2^high compared to TMPRSS2^low MCF-7 cells was
   identified (Fig. [117]3C).

Fig. 3.

   [118]Fig. 3
   [119]Open in a new tab

   Effects of TMPRSS2 overexpression on HLA class I expression, the
   antigen presentation machinery (APM) and interferon (IFN) signaling in
   TMPRSS2-transfected cells and their influence on natural killer (NK)
   cell function. A: Flow cytometry was used to assess HLA-I surface
   expression in TMPRSS2 transfectants and mock control MCF-7 and EA.Hy926
   cells as described in the Methods. The results are displayed as a
   histograms with the mean fluorescence intensity (MFI) of HLA-ABC
   (n = 3). B: The expression of major HLA-I APM components in TMPRSS2
   transfectants was evaluated using qPCR as described in the Methods. C:
   The cytotoxic activity of NK cells from three different donors against
   TMPRSS2^low and TMPRSS2^high MCF-7 cells was evaluated using a CD107a
   degranulation assay. Data are shown as mean ± SE of CD107a
   degranulation upon normalization to the mock-transfected cells. D:
   TMPRSS2 transfectants were examined by qPCR for the expression of
   components of the IFN as well as JAK-STAT signaling pathways. The
   results are shown as an x-fold induction in the expression of these
   signaling components in TMPRSS2 transfectants relative to mock
   controls. E: Representative Western blot analyses of TMPRSS2^high and
   mock-transfected using antibodies directed against IRF1, IRF27, STAT1,
   pSTAT1 and STAT2. Staining with an anti-ACTB antibody served as a
   loading control

   Based on the KEGG pathway enrichment analysis, different JAK-STAT
   signaling molecules, as well as IFN pathway genes, were also
   investigated in TMPRSS2^high vs. TMPRSS2^low tumor cells. A significant
   increase in the mRNA expression of IRF1, IRF9, STAT1, STAT2 and STING
   in TMPRSS2^high transfectants compared to the TMPRSS2^low mock controls
   was found (Fig. [120]3D). The TMPRSS2-mediated altered mRNA levels were
   accompanied by increased protein levels as representatively shown for
   IRF1, IFI27, STAT1, pSTAT1 and STAT2 in TMPRSS2^high vs. TMPRSS2^low
   cells (Fig. [121]3E).

   The link between TMPRSS2 expression and immune response-relevant
   profiles was further validated by in silico analyses of cancer genome
   databases with a positive correlation between TMPRSS2 and the
   expression of HLA-I APM and IFN type I and II pathway genes
   (Supplementary Table [122]3).

TMPRSS2-mediated upregulation of ACE2 expression

   To investigate whether there is an interplay between TMPRSS2 and ACE2,
   the key entry point for SARS-CoV-2 into cells (Fig. [123]4A), the ACE2
   expression was determined in TMPRSS2^high vs. TMPRSS2^low cells.
   TMPRSS2^high EA.Hy926 cells exhibited higher protein levels of ACE2
   (Fig. [124]4B) compared to TMPRSS2^low cells, suggesting a connection
   between TMPRSS2 and ACE2 expression in these cells. Furthermore, a
   correlation analysis of COVID-19 and healthy PBMNCs revealed a
   potential association (r = 0.24; p = 0.026) between TMPRSS2 and ACE2
   (Fig. [125]4C). The upregulation of ACE2 by TMPRSS2 in cancer cells and
   PBMNCs highlights a significant overlap between cancer biology and
   infectious diseases, particularly in the context of the COVID-19
   pandemic.

Fig. 4.

   [126]Fig. 4
   [127]Open in a new tab

   TMPRSS2-mediated upregulation of ACE2 expression. A: ACE2 serves as a
   receptor for the entry of SARS-CoV-2, while TMPRSS2 is involved in the
   activation of the spike (S) protein. These are crucial elements for
   comprehending the pathophysiology of COVID-19. B: ACE2 expression of
   TMPRSS2^high and mock-transfected EA.Hy926 cells was determined by
   Western blot using an anti-ACE2 as well as anti-TMPRSS2 mAb. C: A
   correlation plot illustrates the association of ACE2 and TMPRSS2
   expression in PBMNCs of COVID-19 patients (COVID-19db)

Link of TMPRSS2 overexpression with PD-L1 surface expression

   Since TMPRSS2 plays a significant role in immune activation and
   exhaustion, the expression levels of inhibitory checkpoint molecules,
   including PD-L1 (CD274), PD-1 (PDCD1), CTLA4 and several others
   (specifically ADORA2A, ADORA2B, BTLA, CYBB, HAVCR2, IDO1, LAG3,
   SIGLEC7, VTCN1 and CD276) was analyzed upon SARS-CoV-2 infection and
   TMPRSS2 overexpression (Supplementary Table [128]4). High levels of
   PD-L1 and IDO1 were found in SARS-CoV-2-infected in vitro, Calu3 cells
   and in vivo mouse models. These data were in line with transcriptomic
   analyses of TMPRSS2^high MCF-7 and EA.Hy926 cells demonstrated a marked
   increase in the expression of PD-L1 and IDO1. Furthermore, elevated
   PD-L1 levels in COVID-19 patients were closely linked to high mortality
   rates [[129]14]. TMPRSS2^high MCF-7 and EA.Hy926 cells exhibited higher
   levels of PD-L1 mRNA and protein expression compared to TMPRSS2^low
   control cells. This was determined through qPCR (Fig. [130]5A), Western
   blot analysis (data not shown), and flow cytometry (Fig. [131]5B). The
   increased PD-L1 levels in TMPRSS2-transfected tumor cells were further
   supported by in silico data (COVID19db ID: COVID000010). A correlation
   analysis between TMPRSS2 and PD-L1 in peripheral blood mononuclear
   cells (PBMNCs) from COVID-19 patients and healthy controls
   (Fig. [132]5C) revealed a positive correlation (R = 0.54; p = 7.2e-08).
   These data suggest that the TMPRSS2^high cells partially resemble
   COVID-19 disease conditions and postulate ICPi as a treatment option.

Fig. 5.

   [133]Fig. 5
   [134]Open in a new tab

   Enhanced PD-L1 and IL6 expression in TMPRSS2^high cells. A: mRNA
   expression by qPCR analysis was performed to assess the expression
   levels of PD-L1 in both TMPRSS2^high cells and mock-transfected control
   cells. B: Flow cytometry was used to evaluate PD-L1 surface expression
   in TMPRSS2 transfectants of MCF-7 and EA.Hy926 cells (n = 3). The
   results are presented as MFI. C: A correlation plot depicts the
   relationship between TMPRSS2 and PD-L1 expression in blood samples from
   COVID-19 patients (COVID-19db). D: The supernatants from TMPRSS2^high
   and TMPRSS2^low EA.Hy926 cells were assessed for IL-6 secretion using
   ELISA. The data represents the mean from three independent experiments

Increased secretion of IL-6 upon TMPRSS2 overexpression

   IL-6 has been identified as the primary driver of the cytokine storm
   [[135]40]. Our analysis revealed that elevated IL-6 levels were found
   in the supernatants of TMPRSS2^high cells compared to the TMPRSS2^low
   EA.Hy926 cells (Fig. [136]5D). These data were confirmed by in vivo
   mouse lung data obtained from GEO ID: [137]GSE253635 with significantly
   enhanced IL-6 levels in C57BL6/J wild-type mice following SARS-CoV-2
   infection [[138]36]. Additionally, high IL-6 levels were found in PBMNC
   samples from patients with long COVID-19 (GEO: [139]GSE224615;
   [[140]35]) compared to uninfected PBMCs. In addition to IL-6, 15 other
   cytokines, chemokines, and growth factors were analyzed to provide a
   comprehensive overview of the inflammatory response induced by
   SARS-CoV-2. These include CSF2, IL-2, IL-4, IL-5, IL-1β, IL-9, IL-10,
   IL-12 A, IL-13, IL-17 A, IFN-γ, CCL2, CCL3, CCL4, and TNF
   (Supplementary Table [141]5). Following SARS-CoV-2 infection, C57BL6/J
   mice exhibited increased levels of CCL2, IFN-γ, IL-10, IL-1β and IL-6.

Suppression of TMPRSS2 expression by a JAK2 inhibitor and small interfering
RNA

   Based on the SARS-CoV-2-mediated induction of IFN pathway components,
   TMPRSS2^high MDA-MB-468 cells were treated with the JAK2 inhibitor
   ruxolitinib [[142]41]. Ruxolitinib reduced JAK2 levels (data not shown)
   and decreased TMPRSS2 and ACE2 protein expression (Fig. [143]6A). Using
   a dataset from MCF-7 cells (GEO dataset ID: [144]GSE21618), positive
   correlations of TMPRSS2 gene expression with these of JAK1
   (Fig. [145]6B; R = 0.684; p = 4.88e-21) and JAK2 (Fig. [146]6C;
   R = 1.99; p = 0.017) were found via the R2 genomics platform.

Fig. 6.

   [147]Fig. 6
   [148]Open in a new tab

   Decreased TMPRSS2 levels upon ruxolitinib treatment. A: Western blot
   analyses were performed using antibodies targeting TMPRSS2 and ACE2 in
   cells treated with 300 nM of ruxolitinib. B, C: Correlation plots were
   generated for TMPRSS2 with JAK1 (B) and JAK2 (C) using the MCF-7
   dataset and R2 Genomics. D: Reduction of TMPRSS2 by siTMPRSS2 delivered
   via lipid nanoparticles was evaluated by Western blot analysis with
   antibodies against TMPRSS2 in MDA-MB-468 cells. E, F: Silencing of
   TMPRSS2 resulted in a significant reduction in HLA-I (E) and PD-L1 (F)
   surface expression assessed by flow cytometry

   To further explore the role of TMPRSS2, silencing experiments were
   conducted using siTMPRSS2. As expected, transfection with the
   siTMPRSS2-LNP complex decreased the TMPRSS2 protein levels in
   TMPRSS2^high MDA-MB-468 cells (Fig. [149]6D). This significant
   downregulation of TMPRSS2 correlated with a decrease of both HLA-I
   (Fig. [150]6E) and PD-L1 (Fig. [151]6F) surface expression, confirming
   a crucial role of TMPRSS2 in the modulation of both molecules
   (Fig. [152]5C).

Increased immune cell migration of TMPRSS2high vs. TMPRSS2low MCF-7 cells
after treatment with nivolumab

   In the context of COVID-19, blocking the PD-1 pathway has been shown to
   counteract the immune system abnormalities induced by the virus and
   enhance the body’s immune response against SARS-CoV-2 [[153]42]. To
   investigate whether the expression levels of TMPRSS2 may influence the
   efficacy of anti-PD1 mAb treatments, the immune cell migration, cancer
   cell proliferation and apoptosis were determined over three days of
   co-cultures between TMPRSS2^low or TMPRSS2^high MCF-7 and immune cells
   in the presence and absence of nivolumab. The immunofluorescent images
   captured throughout the three-day time frame post-nivolumab treatment
   revealed a nivolumab-mediated significant increase of the immune cell
   infiltration towards the TMPRSS2^high MCF-7 cells (Fig. [154]7A), but
   only a marginal movement of immune cells towards TMPRSS2^mock/low MCF-7
   cells (Fig. [155]7B). Representative immunofluorescent images
   post-nivolumab treatment from immune cells targeting TMPRSS2^high
   (Fig. [156]7C) and TMPRSS2^low cells (Fig. [157]7D) on day 3 were
   shown. Additionally, the nivolumab treatment has slightly increased
   apoptosis rates of TMPRSS2^high than TMPRSS2^low MCF-7 cells
   (Fig. [158]7E). The nivolumab-mediated inhibition of PD-L1 on
   TMPRSS2^high MCF-7 cells might lead to increased immune cell
   infiltration and facilitate T cell activation. These data suggest the
   therapeutic potential of this treatment in redirecting the immune
   response against these cancer cells.

Fig. 7.

   [159]Fig. 7
   [160]Open in a new tab

   Microfluidic chip-based analyses of nivolumab-mediated effects on
   immune cell migration and cytokine release towards TMPRSS2^high cells.
   A, B: During a 3-day period, immune cells significantly infiltrated
   toward TMPRSS2^high MCF-7 cells (A), but not to TMPRSS2^low cells (B)
   treated with nivolumab. C, D. Representative immunofluorescent images
   of day 3 cancer cells, TMPRSS2^high (C) and TMPRSS2^low (D) marked in
   red, lymphocytes in blue and apoptotic cells in green. E: Percentage of
   apoptotic cells of TMPRSS2^high/vector MCF-7 cells. Nivolumab therapy
   on immune cells showed a marginally higher apoptosis rate in
   TMPRSS2^high compared to TMPRSS2^low cells. F: Higher release of IL-2
   and reduced CCL2 and CCL3 levels in TMPRSS2^high MCF-7 cells when
   compared to mock-transfected cells. CI - Cancer cells co-cultured with
   Immune cells, CIN – CI treated with nivolumab

Link of altered cytokine release and TMPRSS2 expression upon nivolumab
treatment

   The impact of nivolumab on the cytokine release in the cell
   supernatants during the co-culture of PBMNCs with TMPRSS2^high and
   TMPRSS2^low MCF-7 cells was investigated using a human FirePlex^®-96
   key cytokine immunoassay panel. As shown in Fig. [161]7F, treatment of
   TMPRSS2^high and TMPRSS2^low MCF-7 cells with nivolumab decreased the
   release of CCL2 and CCL3. However, there was an increase in IL-2
   secretion in the TMPRSS2^high cells compared to the TMPRSS2^low cells.
   It’s important to note that IL-2 was added during the experiment, which
   may have influenced its secretion. The elevated IL-6 secretion
   (Fig. [162]5E) was slightly but not significantly reduced in the
   presence of TMPRSS2^high cells upon nivolumab (Fig. [163]7F). No
   statistically significant changes of other cytokines were detected in
   the supernatants of untreated and nivolumab-treated TMPRSS2^high cells
   suggesting no interference of ICPi with the cytokine storm
   (Supplementary Table [164]6).

Discussion

   Patients with cancer are more affected by COVID-19 with longer
   hospitalization time and increased mortality [[165]8, [166]43]. So far,
   the impact of additional treatments affecting the immune system, such
   as immunotherapy or immunosuppressive agents, on the severity of
   COVID-19 in tumor patients has not yet been determined [[167]8]. Our
   study showed that TMPRSS2 affected the expression of the IFN signaling
   components including JAK-STAT genes in both tumor cell lines and
   patients’ samples, which is consistent with the COVID-19 genome
   databases (COVID19db; GEO: [168]GSE147507). This is linked to an
   increased HLA-I and PD-L1 expression suggesting that cancer patients
   with COVID-19 might respond more to ICPi treatments directed against
   the PD1/PD-L1 axis (Fig. [169]8).

Fig. 8.

   [170]Fig. 8
   [171]Open in a new tab

   Cells exhibiting elevated levels of TMPRSS2 demonstrated increased
   levels of PD-L1, the IFN pathway, HLA class I antigen presentation
   machinery (APM) and immune responses in comparison to those with lower
   TMPRSS2 levels. This indicates that the expression of TMPRSS2 across
   various model systems and following SARS-CoV-2 infection correlates
   with alterations in host immunogenicity. These alterations may
   influence the efficacy of the PD-1 inhibitor nivolumab, which enhances
   the migration of immune cells and decreases cytokines associated with
   innate immunity (created with BioRender.com)

   Since coronaviruses are considered emerging and re-emerging pathogens
   [[172]44], anti-viral strategies targeting multiple coronaviruses are
   urgently needed. High TMPRSS2 expression levels associated with
   SARS-CoV-2 infection were also frequently found in various cancers.
   Despite TMPRSS2 as well as ACE2 has higher expression levels after the
   onset of COVID-19 and cancers [[173]45], no detailed study is available
   on their expression and interaction in cancer patients infected with
   COVID-19 or any other coronaviruses. Recently, a correlation between
   ACE2 expression and immune response has been reported [[174]46], which
   highlights the potential role of coronavirus target genes in shaping
   the tumor microenvironment (TME) and influencing tumor behavior during
   viral infections. The in vitro tumor models investigated in this study
   showed that TMPRSS2 could enhance ACE2 expression in cancer cells, but
   an in depth understanding of the precise molecular pathways, through
   which TMPRSS2 regulates ACE2, requires further investigations.

   There exists evidence of an IFN-γ component-mediated control of APM
   molecules in tumors [[175]47], but APM components have not been
   extensively studied in the context of COVID-19. Interestingly, an
   upregulation of many APM components, particularly TAP2, was found in
   TMPRSS2^high cells and COVID-19 PBMNCs. Furthermore, KEGG pathway
   analyses indicated an enrichment of several signaling pathways
   associated with virus infection and the NOD-like receptor signaling in
   PBMNCs of patients with COVID-19 disease [[176]48, [177]49]. In a
   multi-cohort study, IFI27 transcription was identified as an early
   predictor for the outcome of COVID-19 patients [[178]50]. Consistent
   with these data, our TMPRSS2^high EA.Hy926 transfectants showed
   increased IFI27 mRNA (Log2FC 3.57; p 3.68E-15) and protein levels. IFNs
   play a dual role in immune responses. They enhance the immune system’s
   ability to eliminate tumor cells, while promoting mechanisms leading to
   inflammation and allowing immune evasion by e.g. upregulation of PD-L1.
   The IFN-mediated increase in PD-L1 expression represents a form of
   “adaptive resistance” [[179]51], which we demonstrated in our results
   with an upregulation of IFN and PD-L1 in cells with high TMPRSS2
   expression and in COVID-19 patients. Furthermore, increased expression
   of IFN signaling pathway molecules along with IL-6 in long COVID-19
   patients indicated a potential role of IFN signaling in the prolonged
   effects in these patients [[180]35]. Upon binding of IFN to its
   receptor, JAK kinases are activated leading to STAT1 phosphorylation
   [[181]52] and consequently to induction of PD-L1 expression [[182]53].
   Interestingly, TMPRSS2^high cells expressed high levels of
   phosphorylated STAT1 thereby triggering PD-L1 surface expression. An
   improved understanding of the connection between PD-L1 expression and
   JAK-STAT signaling might provide novel options for combination
   therapies. The efficacy of PD-1/PD-L1 blockade could be enhanced by the
   use of JAK inhibitors [[183]51]. Indeed, in patients with Hodgkin
   lymphoma [[184]54] and in those with non-small cell lung cancer
   [[185]55], JAK inhibition improved checkpoint blockade immunotherapy.
   Additionally, JAK inhibitors, such as ruxolitinib, have been discussed
   for their potential in treating COVID-19 [[186]24, [187]56] with focus
   on ACE2. This was extended by our study to TMPRSS2, which is a direct
   target of ruxolitinib. Elevated levels of the ICP molecule IDO1,
   alongside PD-L1, in the transcriptome profile of TMPRSS2^high and
   SARS-CoV-2-infected cells were found suggesting exploring the potential
   application of IDO1 inhibitors.

   Growth factors and inflammatory cytokines, such as EGF, IL-6, IFN-γ,
   TNF-α and TGF-β, can induce PD-L1 expression [[188]57, [189]58].
   However, whether anti-PD-1/PD-L1 treatment would be beneficial for
   COVID-19 patients has to be investigated, since this treatment might
   enhance the cytokine storm associated with higher COVID-19 morbidity
   and mortality [[190]59, [191]60]. It is noteworthy that melanoma
   patients with COVID-19 displayed better outcomes when treated with ICPi
   [[192]61, [193]62]. Therefore, anti-PD-1 therapy may also benefit
   SARS-CoV-2-infected patients. Our data indicates that TMPRSS2^high
   cells, which have increased PD-L1 expression, responded to nivolumab,
   demonstrating a TMPRSS2-dependent response along with increased immune
   cell infiltration into cancer cells.

   Cytokines mediate the expansion, activation and trafficking of effector
   lymphocytes, but can also recruit regulatory T cells [[194]63].
   Additionally, TMPRSS2-mediated higher STAT1 expression could regulate
   IFN-γ-induced CCL2 expression [[195]64]. This decline in CCL2 and CCL3
   might revert T cell exclusion and the cytokine storm thereby affecting
   COVID-19 severity. A decreased innate immunity and the release of
   anti-inflammatory cytokines CCL2 and CCL3, respectively, were detected
   in TMPRSS2 transfectants co-cultured with PBMNCs upon nivolumab
   treatment. Targeting TMPRSS2 with ruxolitinib may reduce its expression
   and revert the associated immune suppression, while nivolumab could
   enhance immune cell activity against tumors expressing TMPRSS2. A
   recent clinical trial demonstrated that ruxolitinib in combination with
   nivolumab improved the clinical outcome of patients primarily not
   responding to checkpoint blockade. This result paves the way for novel
   therapeutic strategies for cancer and COVID-19 patients [[196]54]. On
   the other hand, further studies are needed to understand how anti-PD-1
   therapy affects TMPRSS2, cytokine storm, IFN signaling and lymphocyte
   composition and function following SARS-CoV-2 infection.

Conclusions

   Our research indicates a link between TMPRSS2 and different types of
   tumors as well as an effect of SARS-CoV-2 infection on the immune
   response by activating immune-related pathways. Targeting TMPRSS2 as a
   universal coronavirus strategy shows potential for the design of novel
   treatments and vaccines that can offer broad protection against current
   and future coronavirus threats. Our study emphasizes the importance of
   using in vitro disease models and RNA-seq datasets to form hypotheses
   relevant to human disease. Our findings show that treatment with an
   anti-PD-1 antibody improved the infiltration of immune cells and
   inhibited the production of inflammatory cytokines in an in vitro
   TMPRSS2^high model. These results suggest that anti-PD-L1 treatment
   could restrict T cell exhaustion and hinder virus infectivity at an
   early stage of virus entry. Combining TMPRSS2 inhibitors with immune
   modulators, such as PD-1 inhibitors and/or JAK inhibitors, may offer a
   comprehensive approach for the treatment of COVID-19, but the
   therapeutic strategies need to be balanced between enhancing anti-viral
   immunity and preventing excessive inflammation and activation of the
   immune system, which can cause tissue damage and worsen outcomes as
   observed in severe COVID-19 cases.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [197]Supplementary Material 1^ (375.5KB, pdf)

Acknowledgements