Skip to main content

The expression landscape of JAK1 and its potential as a biomarker for prognosis and immune infiltrates in NSCLC

Abstract

Background

Janus-activated kinase-1 (JAK1) plays a crucial role in many aspects of cell proliferation, differentiation, apoptosis and immune regulation. However, correlations of JAK1 with prognosis and immune infiltration in NSCLC have not been documented.

Methods

We analyzed the relationship between JAK1 expression and NSCLC prognosis and immune infiltration using multiple public databases.

Results

JAK1 expression was significantly decreased in NSCLC compared with that in paired normal tissues. JAK1 overexpression indicated a favourable prognosis in NSCLC. In subgroup analysis, high JAK1 expression was associated with a preferable prognosis in lung adenocarcinoma (OS: HR, 0.74, 95% CI from 0.58 to 0.95, log-rank P = 0.017), not squamous cell carcinoma. In addition, data from Kaplan–Meier plotter revealed that JAK1 overexpression was associated with a preferable prognosis in male and stage N2 patients and patients without distant metastasis. Notably, increased levels of JAK1 expression were associated with an undesirable prognosis in patients with stage 1 (OS: HR, 1.46, 95% CI from 1.06 to 2.00, P = 0.02) and without lymph node metastasis (PFS: HR, 2.18, 95% CI from 1.06 to 4.46, P = 0.029), which suggests that early-stage NSCLC patients with JAK1 overexpression may have a bleak prognosis. Moreover, multiple immune infiltration cells, including NK cells, CD8 + T and CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells (DCs), in NSCLC were positively correlated with JAK1 expression. Furthermore, diverse immune markers are associated with JAK1 expression.

Conclusions

JAK1 overexpression exhibited superior prognosis and immune infiltration in NSCLC.

Peer Review reports

Introduction

Lung cancer, as a malignant tumour with high morbidity and mortality, poses a serious threat to people’s physical and mental health [1]. Non-small-cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancer cases [2]. With the advent of precision therapy, lung cancer treatment has entered molecular therapy, including targeted therapy, anti-angiogenesis therapy and immunotherapy [3]. However, the prognosis has not improved significantly, and the 5-year survival rate remains poor [1]. In recent years, clinical studies have shown that immunotherapy (PD-1/L1 monoclonal antibody, CTLA-4 inhibitor) has great potential in the treatment of lung cancer patients without epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutations [4]. Nevertheless, immunotherapy only activates immune cells in a subset of patients. With the continuous exploration of the tumour immune microenvironment (TME), which can directly or indirectly affect the development of tumours, including promoting tumour angiogenesis, changing the biological characteristics of the tumour, promoting immune escape, and even regulating the activity of cancer stem cells (CSCs) [5, 6]. Many studies have found that TAMs (tumour-associated macrophages), TILs (tumour infiltrating lymphocytes) and TINs (tumour-infiltrating neutrophils) in the TME can affect the efficacy of immunotherapy [7, 8]. Hence, it is imperative to find immune infiltration-related biomarkers that are related to the prognosis of NSCLC.

Janus-activated kinase (JAK) is an inactive tyrosine protein kinase that consists of four family members, including JAK1, JAK2, TYK2, and JAK3 [9]. JAKs approach each other and are activated by interactive tyrosine phosphorylation, ultimately leading to signal transducer and activator of transcription (STAT) proteins forming a homo/heterodimer that is incorporated into the nucleus and binding to the target gene promoter to activate transcription and expression [10]. Previous studies have shown that the JAK1/STAT3 pathway is widely involved in many significant biological processes, such as cell proliferation, differentiation, apoptosis and immune regulation [11,12,13]. JAK family kinases play an essential role in cytokine signalling. Functionally acquired JAK1 mutations can encourage the development of cancers, especially leukaemia. Abnormal JAK1 expression either promotes or suppresses tumour growth [10, 14, 15]. Chen et al. [10] showed that high expression of JAK1 mRNA was associated with TNM (Tumor, Node, Metastasis) stage and superior prognosis of breast cancer. In addition, infiltration and enrichment of immunoregulatory cells were significantly positively correlated with JAK1 expression. In contrast, Zhang et al. [16] showed that JAK1 signal activation could promote the proliferation of bladder cancer cells and lead to a poor prognosis. Hu et al. also indicated that JAK1/STAT3 plays a crucial role in ovarian cancer as a pro-oncogenic signalling pathway [17]. Whether JAK1 expression is involved in the prognosis and the level of immune infiltration in NSCLC still needs to be further explored.

In our descriptive study, we explored the expression landscape of JAK1 in NSCLC and its relationship with prognosis using shared databases, including TIMER, GEPIA, Kaplan–Meier Plotter and PrognoScan. We also visualized the relationship between JAK1 and immune infiltration using TIMER and TISIDB. Moreover, correlations between JAK1 expression and multiple gene marker sets related to immune infiltrates were also analysed via TIMER and GEPIA.

Materials and methods

TIMER database analysis

The TIMER (Tumour Immune Estimation Resource) web server is a comprehensive resource for the systematic analysis of immune infiltrates across diverse cancer types. (https://cistrome.shinyapps.io/timer/) [18]. The abundances of six immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells) were estimated by the TIMER algorithm. We evaluated the correlation between JAK1 expression levels and various immune infiltrating cells via the TIMER algorithm. In addition, JAK1 expression profiles across various tumour samples and paired normal tissues from the TCGA data in TIMER were also determined. Finally, to further identify other potential subtypes of immune cell infiltration, we also analysed the correlation between JAK1 expression and diverse immune cell markers, including monocytes, tumour-associated macrophages (TAMs), M1 macrophages, M2 macrophages, CD8+ T cells, B cells, neutrophils, dendritic cells, natural killer (NK) cells, T-helper 1 (Th1) cells, T-helper 2 (Th2) cells, Tregs and exhausted T cells (https://www.rndsystems.com/cn/resources/cell-markers/immune-cells). Tumour purity was also determined. The gene expression level was described in terms of log2TAM. JAK1 expression was drawn in the x-axis, while marker genes were drawn in the y-axis. A scatterplot was used to describe the specific connection between every immune gene marker and JAK1 expression.

TISIDB analysis

TISIDB is also a web portal for tumour and immune system interaction, which integrates multiple heterogeneous data types. (http://cis.hku.hk/TISIDB/index.php) [19]. We explored the correlation between JAK1 expression in NSCLC and the abundance of multiple immune cells, including activated CD4 T cells (Act_CD4), activated dendritic cells (Act_DCs), immature dendritic cells (iDCs), neutrophils, natural killer cells (NKs), plasmacytoid dendritic cells (pDCs), central memory CD4 cells (Tcm_CD4), and effector memory CD8 cells (Tem_CD8). The relative abundance of each immune cell was inferred by using gene set variation analysis (GSVA) based on the gene expression profile. JAK1 expression was drawn on the x-axis, while the abundance of immune cells was drawn on the y-axis. A scatterplot was used to display the correlation between the abundance of each immune cell and JAK1 expression.

GEPIA database analysis

To further verify the gene marker associated with immune infiltration in NSCLC. We used the public database Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html) [20], which analyses the RNA sequencing expression from the TCGA and GTEx projects of 9736 tumours and 8587 normal samples. The correlation coefficient was determined by the Spearman method. The tumour and normal tissue datasets were used for analysis. JAK1 expression profiles across LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma) samples and paired normal tissues from GEPIA were also analysed.

Prognostic analysis

We used public databases including Kaplan–Meier Plotter (https://kmplot.com/analysis/) [21] and PrognoScan (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) [22] to examine the relationship between JAK1 expression level and NSCLC prognosis. The Kaplan–Meier plotter is competent for assessing the effect of 54,000 genes on prognosis in 21 cancer types. Sources included the GEO, EGA, and TCGA databases. The hazard ratio (HR) and its 95% confidence interval (95% CI) for OS (overall survival) and PFS (progression-free survival) in NSCLC were calculated. The log-rank P value was likewise computed.

Similarly, the prognostic database PrognoScan was designed to analyse the correlation between JAK1 expression and overall survival (OS). The threshold was set as a Cox P value < 0.05.

Statistical analysis

The results examined in TIMER and GEPIA are displayed with P values determined by t tests, fold changes, and gene ranks. Survival outcomes were presented with Kaplan–Meier plots and PrognoScan, and the results are displayed with HR and Cox P values from a log-rank test. The correlation between JAK1 expression and each gene marker was assessed by Spearman’s correlation test and statistical significance. The strength of the correlation was defined as follows: 0.00–0.19 “very weak”, 0.20–0.39 “weak”, 0.40–0.59 “moderate”, 0.60–0.79 “strong”, and 0.80–1.0 “very strong”. For all analyses, a P value less than 0.05 indicates statistical significance.

Results

JAK1 expression in multiple human tumours

We evaluated the differences in JAK1 expression in various human tumour tissues and paired normal tissues using RNA sequencing data from the TCGA. The detailed expression of JAK1 in the tumour and adjacent tissues is shown in Fig. 1A. JAK1 expression was significantly decreased in BLCA (bladder urothelial carcinoma), BRCA (breast invasive carcinoma), COAD (colon adenocarcinoma), KICH (kidney chromophobe), LUAD, LUSC, PRAD (prostate adenocarcinoma), READ (rectum adenocarcinoma), and UCEC (uterine corpus endometrial carcinoma) compared to that in adjacent normal tissues, while the expression of JAK1 was significantly higher in CHOL (cholangiocarcinoma), ESCA (oesophageal carcinoma), HNSC (head and neck squamous cell carcinoma), KIRC (kidney renal clear cell carcinoma), KIRP (kidney renal papillary cell carcinoma), LIHC (liver hepatocellular carcinoma), STAD (stomach adenocarcinoma), and THCA (thyroid carcinoma) than that in adjacent normal tissues.

Fig. 1
figure1

JAK1 expression profile in various types of human tumours. A Human JAK1 expression profile across various tumour samples and paired normal tissues from the TCGA data in TIMER. B Human JAK1 expression profile in NSCLC from the GEPIA database. The ordinate of B indicates the expression level of JAK1 (log2 TPM). LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, NSCLC non-small-cell lung cancer, TPM transcripts per million, N normal, T tumour. *P < 0.05, **P < 0.01, ***P < 0.001

To further evaluate the expression patterns of JAK1 in NSCLC, the GEPIA database was further selected. Similar results were likewise obtained, namely, JAK1 expression in LUAD and LUSC was significantly lower than that in the paired normal tissues (Fig. 1B).

JAK1 expression predicts the prognosis of NSCLC

Next, we explored the prognostic value of JAK1 for NSCLC by adopting two public databases. First, we investigated JAK1 expression and the prognosis of NSCLC, LUAD and LUSC using Kaplan–Meier Plotter, which principally focused on the strength of the information from the GEO, EGA and TCGA miRNA gene chips. The results showed that high JAK1 expression indicated a favourable prognosis in NSCLC (OS: HR, 0.62, 95% CI from 0.53 to 0.74, log-rank P < 0.001; PFS: HR, 0.65, 95% CI from 0.50 to 0.86, log-rank P = 0.002). In the subgroup analysis, the high expression of JAK1 in LUAD lasted longer in OS (HR: 0.74, 95% CI from 0.58 to 0.95, log-rank P = 0.017), but there was no benefit in PFS (HR: 0.83, 95% CI from 0.60 to 1.14, log-rank P = 0.24). In LUSC, high expression of JAK1 was associated with longer duration of PFS (HR: 0.65, 95% CI from 0.39 to 1.09, log-rank P = 0.097), while the difference was not statistically significant. In addition, there was no benefit in OS (HR: 0.95, 95% CI from 0.69 to 1.29, log-rank P = 0.73). (Fig. 2).

Fig. 2
figure2

Kaplan–Meier survival curves comparing high and low expression of JAK1 in NSCLC. A, B OS and PFS survival curves of NSCLC (n = 1144, n = 596). C, D OS and PFS survival curves of LUAD (n = 672, n = 443). E, F OS and PFS survival curves of LUSC (n = 271, n = 141). OS overall survival, PFS progression-free survival, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, NSCLC non-small-cell lung cancer, HR hazard ratio

Next, we investigated the association of JAK1 expression and prognosis with distinct clinicopathological features in NSCLC (Table 1). JAK1 overexpression related to superior OS and PFS in males (HR: 0.64, 0.62, 95% CI from 0.52 to 0.79, P < 0.001) rather than females. In addition, the higher expression of JAK1 is associated with preferable OS in patients with N2 lymph node metastasis (HR: 0.39, 95% CI from 0.17 to 0.86, P = 0.016) without distant metastasis (HR: 0.73, 95% CI from 0.56 to 0.93, P = 0.013) of NSCLC. Notably, overexpression of JAK1 is associated with undesirable prognosis in patients with stage 1 NSCLC (OS: HR, 1.46, 95% CI from 1.06 to 2.00, P = 0.02) and without lymph node metastasis (PFS: HR, 2.18, 95% CI from 1.06 to 4.46, P = 0.029), which implicit early NSCLC patients with JAK1 overexpression may have a poor prognosis. Regrettably, there were no statistically significant differences between JAK1 expression and prognosis in females, stage 2 to 3, stage T1 to T4, N1 lymph node metastasis or prior chemotherapy. The exact survival time is shown in Additional file 1: Table S1.

Table 1 Association between JAK1 expression and prognosis with different clinicopathological features of NSCLC by Kaplan–Meier plotter

Finally, we selected the PrognoScan database to further verify the relationship between JAK1 expression and prognosis in NSCLC. Five cohorts containing a total of 530 patients with NSCLC and LUAD showed that high expression of JAK1 was associated with favourable OS (Table 2).

Table 2 Survival analysis of JAK1 mRNA in NSCLC from the PrognoScan database

Correlation of JAK1 expression and immune infiltration

Tumour infiltrating lymphocytes (TILs) are closely related to prognosis and subsequent immunotherapy in lung cancer [23, 24]. We investigated the correlation between JAK1 expression level and immune cell infiltration in LUAD and LUSC from TIMER. The results showed that JAK1 expression was negatively correlated with tumour purity (r = − 0.229, P = 2.73e-07) and significantly positively correlated with infiltrating levels of B cells (r = 0.155, P = 6.20e−04), CD8+ T cells (r = 0.307, P = 4.41e−12), CD4+ T cells (r = 0.422, P = 2.42e−22), macrophages (r = 0.342, P = 9.91e−15), neutrophils (r = 0.459, P = 1.41e−26), and dendritic cells (r = 0.479, P = 2.25e−29). Similar results were also observed in LUSC. JAK1 expression was negatively correlated with tumour purity (r = − 0.309, P = 5.34e−12) and significantly positively correlated with infiltrating levels of B cells (r = 0.224, P = 8.82e−07), CD8+ T cells (r = 0.26, P = 9.26e−09), CD4+ T cells (r = 0.517, P = 7.50e−34), macrophages (r = 0.405, P = 2.92e−20), neutrophils (r = 0.522, P = 1.36e−34), and dendritic cells (r = 0.5, P = 2.20e−31) (Fig. 3).

Fig. 3
figure3

TIMER database showing the relationship between JAK1 expression level and immune infiltration in LUAD and LUSC. A JAK1 expression was negatively correlated with tumour purity and significantly positively correlated with infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in LUAD. B JAK1 expression was negatively correlated with tumour purity and significantly positively correlated with infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in LUSC. LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma. A P value less than 0.05 indicated statistical significance

In addition, the public database TISIDB also explored the correlation between the abundance of multiple immune cells and JAK1 expression in NSCLC. The enrichment of diversified immune cells, such as Act_CD4, Act_DCs, iDCs, neutrophils, NK cells, pDCs, Tcm_CD4 and Tem_CD8, was positively correlated with JAK1 expression in LUAD and LUSC. What needs illustration is that JAK1 expression has no significant corrections with infiltrating levels of Act_CD4 in LUSC. For details, please refer to Fig. 4 and Additional file 1: Fig S1.

Fig. 4
figure4

Association between JAK1 expression level and immune cell infiltration in LUAD from the TISIDB web portal (517 samples). AH JAK1 expression was significantly positively correlated with infiltrating levels of Act_CD4, Act_DCs, iDCs, neutrophils, NK cells, pDCs, Tcm_CD4 and Tem_CD8. LUAD lung adenocarcinoma, Act_CD4 activated CD4 T cells, Act_DCs activated dendritic cells, iDCs immature dendritic cells, NK cells natural killer cells, pDCs plasmacytoid dendritic cells, Tcm_CD4 central memory CD4 cells, Tem_CD8 effector memory CD8 cells. A P value less than 0.05 indicated statistical significance

Correlations between JAK1 expression and immune gene markers

To further understand the interaction between JAK1 expression and TME in NSCLC. We further explored the potential correlation between JAK1 and immune gene markers in the public databases TIMER and GEPIA (Tables 3, 4). These gene markers depicted diverse immune infiltration cells, including monocytes, TAMs, M1 macrophages, M2 macrophages, CD8+ T cells, B cells, neutrophils, dendritic cells and NK cells. In addition, various T cells, including Th1, Th2, Tregs, and T cell exhaustion, which play different functions in the TME, were included. Although they were adjusted for tumour purity, most immune markers remained significantly related to JAK1 expression levels in LUAD and LUSC.

Table 3 Correlation analysis between JAK1 and diverse immune gene markers in LUAD and LUSC from the TIMER database
Table 4 Correlation analysis between JAK1 and relevant genes and markers of monocytes and macrophages in GEPIA

Interestingly, the results from TIMER and GEPIA showed that most gene sets of monocytes, M1 macrophages, and TAMs were significantly associated with JAK1 expression levels in LUAD. However, we discovered that JAK1 expression was also associated with most gene sets of monocytes and TAMs rather than M1 macrophages. Notably, the majority chemokine ligand, which induced cells of the immune system to enter the site of infection, CCL-2, CD80 and CD68 of TAMs, IRF5 and NOS2 of M1, CD163 and MS4A4A of M2 were strongly related to JAK1 expression in LUAD (all P value < 0.0001). These consequences suggest that JAK1 may play a vital role in the TME by regulating the function of macrophages. In addition, some of the gene markers, such as MPO, CCR7 and CD11b (ITGAM), of neutrophils and CD8A of CD8+ T cells were associated with JAK1 expression in LUAD and LUSC.

Moreover, the vast majority of gene sets of dendritic cells, including HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, BDCA-1, BDCA-4 and CD11C, were positively correlated with JAK1 expression levels in LUAD and LUSC. These results indicated that LAYN may regulate DCs to play a major role in the TME. Regretfully, nearly all of the gene markers of NK cells had no correlation with JAK1 expression levels. Furthermore, we investigated the relationship between JAK1 expression and gene sets of Tregs and T cell exhaustion. All gene sets suggested a positive correlation with JAK1 expression. Finally, immune checkpoints such as PD-1, CTLA4, LAG3 and TIM3 were strongly connected with the level of JAK1 expression, which suggested that JAK1 may play a role in immunotherapy for NSCLC. Further molecular biology experiment verification is needed.

Discussion

The JAK1/STAT signalling pathway, as a stimulant that is intimately related to the physiological function of interferon, plays a significant role in cell growth, differentiation, immune regulation and other aspects [11, 25, 26]. The exhaustive function of JAK1 in NSCLC has not yet been clarified. Here, we report the expression profile of JAK1 and its association with prognosis and immune infiltration in NSCLC. We found that JAK1 was expressed at low levels in NSCLC, and its expression level was positively correlated with the prognosis of NSCLC, especially in LUAD. Interestingly, JAK1 overexpression was associated with preferable survival in males, stage N2 patients and patients without distant metastasis. In addition, increased levels of JAK1 expression are associated with undesirable survival in patients with earlier stages (stage 1 and N0), suggesting that early-stage NSCLC patients with JAK1 overexpression may have a bleak prognosis. Moreover, diverse immune infiltration cells and gene sets were positively correlated with JAK1 expression level. Hence, to the best of our knowledge, our study is the first to reveal the potential mechanism by which JAK1 functions in the TME and acts as a prognostic biomarker of NSCLC.

The TME plays a crucial role in the gene expression and clinical efficacy of tumour tissues, which are prerequisites and guarantees tumour immune escape [27]. The TME refers to the sum of various immune-related factors, mainly consisting of immune cells and immune-related molecules. In our study, we found that JAK1 expression was significantly positively correlated with the infiltration of various immune cells (monocytes, neutrophils, B cells, dendritic cells, TAMs) in LUAD and LUSC. Presently, the antitumour function of manifold cells has been extensively recognized, especially CD8+ T cells [28], whose number reflects the immune system's ability to kill tumour cells to some extent. Moreover, CD8+ T cell density was positively correlated with the efficacy of immune checkpoint inhibitors (ICIs) in NSCLC and melanoma [29, 30]. This finding may provide an early indication for the efficacy of immunotherapy for NSCLC.

Another significant part of our study is that diverse gene sets were positively correlated with JAK1 expression levels. First, M1 macrophage-related gene markers, such as IRF5 and NOS2, and the gene marker CD163 of M2 macrophages were strongly correlated with JAK1 expression. These findings suggested that JAK1 may play a role in regulating TAM polarization in the TME. Second, overexpression of JAK1 is associated with a variety of T helper cells (Th1, Th2). This intense correlation may indicate that JAK1 regulates T cell function in the immune microenvironment of NSCLC. Third, our study showed a significant correlation between Treg activation (FOXP3, STAT5B, TGFB1, CCR8, CD25 in LUAD and LUSC) and induced T cell exhaustion (PD-1, CTLA-4, TIM-3 in LUAD and LUSC) and JAK1 overexpression. PD-1 (programmed death receptor 1) is a vital immunosuppressive molecule expressed on the surface of T cells that regulates the immune system and promotes tolerance by downregulating the immune system’s response to human cells and by suppressing the inflammatory activity of T cells [31]. Additionally, CTLA-4 and Tim-3 are expressed on regulatory T cells and exhausted T cells as crucial receptor proteins, respectively [32, 33], and both are significantly positively correlated with JAK1 expression. These results suggest that JAK1 plays a potential role in recruiting immune-infiltrating cells in the TME of NSCLC.

Recent studies provide possible mechanisms which explains why JAK1 overexpression correlates with immune infiltration and superior prognosis. Previous studies have shown that JAK1 overexpression can lead to the activation of downstream interferon-stimulated genes, which can eventually exert a range of antitumour effects [34, 35]. These include increased antigen presentation by inducing proteasome subunits, activating transporters associated with antigen processing (TAP), stimulating major histocompatibility complex (MHC) molecules to be involved in antigen recognition and promoting chemokine production to exploit a first-hand antitumour role [36]. Remarkably, numerous studies have revealed that loss-of-function JAK1 mutations are insinuative of immune evasion [11, 37, 38]. Research by Shin et al. [35] showed that JAK1 mutations could induce primary resistance to PD-1 inhibitors in melanoma and colon cancer patients. Rodig et al. [39] also indicated that loss of JAK1 caused perinatal death in mice. Luo et al. [40] have shown that the response of melanoma to PD-L1 inhibitor immunotherapy requires JAK1 signaling, which may be related to its potentiated IFN-γ response in vivo and in vitro. Besides, researchers also point out that human melanoma cell lines are insensitive to interferon (IFN)-induced antitumor effects after JAK1/2 knockout [41]. Consequently, JAK1 may regulate immune-related pathways that affect the prognosis and immune infiltrates of NSCLC. Concrete mechanisms have yet to be explored.

However, the shortcomings of our descriptive study should be noted. First, the sequencing data and tumour tissue chips are based on a variety of platforms and databases, and systematic errors and bias are inevitable. Second, our study analysed only JAK1 expression and immune cell infiltration using a variety of databases, which still needs to be verified by specific in vitro experiments. Finally, the precise regulatory pathway of JAK1 in the TME of NSCLC still needs to be further explored.

In summary, the elevated expression of JAK1 is associated with superior prognosis and abundant immune cell infiltration in NSCLC. These findings may lay the foundation for immunotherapy for NSCLC.

Availability of data and materials

All data generated or analysed during this study are included in this article.

References

  1. 1.

    William CB, Caroline C, Timothy RC, et al. Lung cancer incidence and mortality with extended follow-up in the national lung screening trial. J Thorac Oncol. 2019;14(10):1732–42.

    Article  Google Scholar 

  2. 2.

    Shen K, Cui J, Wei Y, et al. Effectiveness and safety of PD-1/PD-L1 or CTLA4 inhibitors combined with chemotherapy as a first-line treatment for lung cancer: a meta-analysis. J Thorac Dis. 2018;10(12):6636–52.

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Camidge DR, Doebele RC, Kerr KM. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol. 2019;16(6):341–55.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Soukalas N, Kiakou M, Tsapakidis K, et al. PD-1 and PD-L1 as immunotherapy targets and biomarkers in non-small cell lung cancer. J BUON. 2019;24(3):883–8.

    Google Scholar 

  5. 5.

    Khan MAW, Ologun G, Arora R, et al. Gut microbiome modulates response to cancer immunotherapy. Dig Dis Sci. 2020;65(3):885–96.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Lei X, Lei Y, Li JK, et al. Immune cells within the tumor microenvironment: biological functions and roles in cancer immunotherapy. Cancer Lett. 2020;470:126–33.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    De Palma M, Biziato D, Petrova TV. Microenvironmental regulation of tumour angiogenesis. Nat Rev Cancer. 2017;17(8):457–74.

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Li X, Yao W, Yuan Y, et al. Targeting of tumour-infiltrating macrophages via CCL2/CCR2 signalling as a therapeutic strategy against hepatocellular carcinoma. Gut. 2017;66(1):157–67.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Kleppe M, Kwak M, Koppikar P, et al. JAK-STAT pathway activation in malignant and nonmalignant cells contributes to MPN pathogenesis and therapeutic response. Cancer Discov. 2015;5(3):316–31.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Chen B, Lai J, Dai D, et al. JAK1 as a prognostic marker and its correlation with immune infiltrates in breast cancer. Aging (Albany NY). 2019;11(23):11124–35.

    CAS  Article  Google Scholar 

  11. 11.

    Witalisz-Siepracka A, Klein K, Prinz D, et al. Loss of JAK1 drives innate immune deficiency. Front Immunol. 2019;9:3108.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Wehde BL, Rädler PD, Shrestha H, et al. Janus kinase 1 plays a critical role in mammary cancer progression. Cell Rep. 2018;25(8):2192-2207.e5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Chen W, Wu G, Zhu Y, et al. HOXA10 deteriorates gastric cancer through activating JAK1/STAT3 signaling pathway. Cancer Manag Res. 2019;11:6625–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Orpaczy E, Tripolt S, Hoelbl-Kovacic A, et al. Aggressive B-cell lymphomas in patients with myelofibrosis receiving JAK1/2 inhibitor therapy. Blood. 2018;132(7):694–706.

    Article  CAS  Google Scholar 

  15. 15.

    Mohrherr J, Haber M, Breitenecker K, et al. JAK-STAT inhibition impairs K-RAS-driven lung adenocarcinoma progression. Int J Cancer. 2019;145(12):3376–88.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Zhang N, Li F, Gao J, et al. Osteopontin accelerates the development and metastasis of bladder cancer via activating JAK1/STAT1 pathway. Genes Genom. 2020;42(4):467–75.

    CAS  Article  Google Scholar 

  17. 17.

    Wen W, Liang W, Wu J, et al. Targeting JAK1/STAT3 signaling suppresses tumor progression and metastasis in a peritoneal model of human ovarian cancer. Mol Cancer Ther. 2014;13(12):3037–48.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Ru B, Wong CN, Tong Y, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–2.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–102.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Lánczky A, Nagy Á, Bottai G, et al. miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 2016;160(3):439–46.

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Mizuno H, Kitada K, Nakai K, et al. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genom. 2009;2:18.

    Article  CAS  Google Scholar 

  23. 23.

    Oja AE, Piet B, van der Zwan D, et al. Functional heterogeneity of CD4+ tumor-infiltrating lymphocytes with a resident memory phenotype in NSCLC. Front Immunol. 2018;9:2654.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. 24.

    Castaneda CA, Castillo M, Aliaga K, et al. Level of tumor-infiltrating lymphocytes and density of infiltrating immune cells in different malignancies. Biomark Med. 2019;13(17):1481–91.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Danziger O, Pupko T, Bacharach E, Ehrlich M. Interleukin-6 and interferon-α signaling via JAK1-STAT differentially regulate oncolytic versus cytoprotective antiviral states. Front Immunol. 2018;9:94.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Stark GR, Darnell JE Jr. The JAK-STAT pathway at twenty. Immunity. 2012;36(4):503–14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Osipov A, Saung MT, Zheng L, Murphy AG. Small molecule immunomodulation: the tumor microenvironment and overcoming immune escape. J Immunother Cancer. 2019;7(1):224.

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Jansen CS, Prokhnevska N, Master VA, et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature. 2019;576(7787):465–70.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Althammer S, Tan TH, Spitzmüller A, et al. Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy. J Immunother Cancer. 2019;7(1):121.

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Wang L, Ma Q, Yao R, et al. Current status and development of anti-PD-1/PD-L1 immunotherapy for lung cancer. Int Immunopharmacol. 2020;79:106088.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Formenti SC, Rudqvist NP, Golden E, et al. Radiotherapy induces responses of lung cancer to CTLA-4 blockade. Nat Med. 2018;24(12):1845–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Datar I, Sanmamed MF, Wang J, et al. Expression analysis and significance of PD-1, LAG-3, and TIM-3 in human non-small cell lung cancer using spatially resolved and multiparametric single-cell analysis. Clin Cancer Res. 2019;25(15):4663–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Kaleviste E, Saare M, Leahy TR, et al. Interferon signature in patients with STAT1 gain-of-function mutation is epigenetically determined. Eur J Immunol. 2019;49(5):790–800.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Shin DS, Zaretsky JM, Escuin-Ordinas H, et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 2017;7(2):188–201.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Bach EA, Aguet M, Schreiber RD. The IFN gamma receptor: a paradigm for cytokine receptor signaling. Annu Rev Immunol. 1997;15:563–91.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Albacker LA, Wu J, Smith P, et al. Loss of function JAK1 mutations occur at high frequency in cancers with microsatellite instability and are suggestive of immune evasion. PLoS ONE. 2017;12(11):e0176181.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Zaretsky JM, Garcia-Diaz A, Shin DS, et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med. 2016;375:819–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Rodig SJ, Meraz MA, White JM, et al. Disruption of the Jak1 gene demonstrates obligatory and nonredundant roles of the Jaks in cytokine-induced biologic responses. Cell. 1998;93(3):373–83.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Luo N, Formisano L, Gonzalez-Ericsson PI, et al. Melanoma response to anti-PD-L1 immunotherapy requires JAK1 signaling, but not JAK2. Oncoimmunology. 2018;7(6):e1438106.

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Torrejon DY, Abril-Rodriguez G, Champhekar AS, et al. Overcoming genetically based resistance mechanisms to PD-1 blockade. Cancer Discov. 2020;10(8):1140–57.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by Anhui Provincial Special Project of Central Government Guiding Local Science and Technology Development of China (Grant No. 201907d07050001); Funding of “Peak” Training Program for Scientific Research of Yijishan Hospital, Wannan Medical College (Grant No. GF2019J03).

Author information

Affiliations

Authors

Contributions

KS and YW wrote the main manuscript text. WL, TL and YS mainly involved in the design of articles and financial support. XJ, ZL, PZ, XW, and MF help with the making of charts and diagrams for the article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Weihua Lu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Association between JAK1 expression and prognosis with different clinicopathological features of NSCLC by Kaplan-Meier plotter (specific survival data). Fig. S1. Correlation between JAK1 expression level and immune cell infiltration in LUSC from the TISIDB web portal (501 samples). (A-H) JAK1 expression had no significant correlation with infiltrating levels of Act_CD4 and was significantly positively correlated with infiltrating levels of Act_DCs, iDCs, neutrophils, NK cells, pDCs, Tcm_CD4 and Tem_CD8. LUSC, lung squamous cell carcinoma; Act_CD4, activated CD4 T cells; Act_DCs, activated dendritic cells; iDCs, immature dendritic cells; NK cells, natural killer cells; pDCs, plasmacytoid dendritic cells; Tcm_CD4, central memory CD4 cells; Tem_CD8, effector memory CD8 cells. A P value less than 0.05 indicated statistical significance.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shen, K., Wei, Y., Lv, T. et al. The expression landscape of JAK1 and its potential as a biomarker for prognosis and immune infiltrates in NSCLC. BMC Bioinformatics 22, 471 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12859-021-04379-y

Download citation

Keywords

  • JAK1
  • Immune infiltrating
  • Prognosis
  • NSCLC