Upregulation of Ferroptosis-Related Fanconi Anemia Group D2 is a Poor Prognostic Factor and Indicator of Tumor Immune Cell Infiltration in Lung Adenocarcinoma

doi:10.21203/rs.3.rs-1311625/v1

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Upregulation of Ferroptosis-Related Fanconi Anemia Group D2 is a Poor Prognostic Factor and Indicator of Tumor Immune Cell Infiltration in Lung Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-1311625/v1

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Background: Fanconi anemia (FA) group D2 (FANCD2) is a ferroptosis-related gene crucial for DNA damage repair and negatively regulates ferroptosis. Our study aimed to evaluate its prognostic value as well as its association with ferroptosis and immune infiltration in lung adenocarcinoma (LUAD).

Methods: Transcriptome sequencing data and clinical information, three independent cohorts, as well as immunohistochemistry, were collected from the TCGA, GEO, and HPA databases, respectively. Univariate and multivariate analyses were used to assess the correlations between FANCD2 expression and overall survival or clinicopathological parameters. cBioPortal was utilized to investigate the FANCD2 alteration status. Gene and protein networks based on FANCD2 interactions were generated using GeneMANIA and STRING, respectively. Based on the CancerSEA database, the function of FANCD2 was explored at the single-cell level. The relationships between FANCD2 expression levels and tumor-infiltrating immune cells and their equivalent gene signatures were analyzed by TIMER, GEPIA, TISIDB, and ssGSEA databases. CIBERSORT was used to analyze the relevance of the infiltration of 24 types of immune cells.

Results: The results revealed that FANCD2 expression was significantly upregulated in LUAD and lung squamous cell carcinoma (LUSC) tissues than in normal tissues. Further, the overexpression of FANCD2 was closely associated with poor survival for LUAD patients but not for LUSC patients. FANCD2 expression levels were related to tumor-infiltrating immune cells and their matching gene signatures, including CD8+ T cells, NK cells, DC cells, and Th2 cells in cases of LUAD.

Conclusion: FANCD2 was identified as a crucial molecule underlying the synergistic effects of ferroptosis and immunotherapy for LUAD patients.

FANCD2

Lung adenocarcinoma

Ferroptosis

Tumor-infiltrating immune cells

Biomarker

Prognosis

In the past decades, lung cancer has remained the major contributing factor to cancer-related deaths worldwide. According to available data, 2.2 million new cases of lung cancer are diagnosed each year, with 1.8 million people dying from the disease annually[1]. Non-small cell lung cancer (NSCLC), which includes LUADand LUSC, contributes to approximately 85% of all lung cancer cases. However, the majority of patients are in an advanced, unresectable stage of the disease at the time of diagnosis[2], which is associated with a low overall median 5-year survival rate of 15%[3]. The incidence and mortality of LUAD are increasing; it has now surpassed squamous cell carcinoma to become the most common histological subtype of NSCLC[4,5].Despite the active treatment measures available for LUAD, it has the highest mortality rate among all cancers; this might be associated with its tendency to metastasize at an early stage[6-8]. Thus, it is of critical importance to develop novel and more effective therapeutic strategies for LUAD. For this, it is vital to more intensively probe the molecular pathology of the disease.

Ferroptosis is an iron-dependent form of regulated cell death induced by iron-dependent lipid peroxidation owing to metabolic dysfunction;it is distinct from apoptosis, cell necrosis, and autophagy[9,10]. The ferroptosis pathway can restrain tumor growth and induce cancer cell death. Its induction has thus received widespread attention as a potential novel antitumor treatment strategy[11-13]. Inducing ferroptosis has been reported to suppress LUAD by regulating lipid peroxidation to promote tumor cell death[14-16]. Ferroptosis is also connected to immunomodulation and immune evasion, in particular through extensive crosstalk between cancer cells and infiltrating immune cells. Different signals released during ferroptosis that occur in cancer cells can trigger macrophage-mediated phagocytosis in vitro[17]. In vivo experiments have also confirmed that blocking ferroptosis activity suppresses the ability of CD8⁺ T cells to kill cancer cells[18].

With the rapid advances in high-throughput sequencing technologies and transcriptome sequencing (RNA sequencing [RNA-seq]), an increasing number of key driver oncogenes are being discovered. However, it is necessary to identify additional key driver genes, particularly those affecting the tumor immune microenvironment (TME) in LUAD. FANCD2 is a nuclear protein that participates in DNA damage repair via the FA pathway[19]. FANCD2 depletion enhances interstrand crosslink (ICL) agent-induced DNA damage sensitivity andpromotes apoptosis in lung cancer cells by inhibiting the FA pathway[20-22]. In addition,FANCD2 negatively regulates ferroptosis by regulating iron metabolism-related genes and/or protein expression and lipid peroxidation[23]. However, the molecular mechanisms of FANCD2 governing the regulation of the immune response in LUAD are still unclear.

This study aimed to explore the association between FANCD2 expression and clinical information and prognosis in LUAD. Based on bioinformatics techniques, the results showed that FANCD2 expression regulated the level of tumor-infiltrating immune cells through multiple pathways, which contributed tothe formation of the immunosuppressive microenvironment. Therefore, this study thus anticipated presenting promising potential therapeutic strategies for LUAD based on FANCD2.

Hub genes associated with ferroptosis and NSCLC

In total, 173 genes in the FerrDb and 2,414, 3,322, and 1,830 differentially expressed genes (DEGs) in GSE19188, GSE75037, and GSE116959, respectively were identified. As shown in Fig. 1, 17 candidate genes comprised the intersection of the four datasets. Of these, the top five targets were nicotinamide adenine dinucleotide phosphate (NAD[P]H) dehydrogenase (quinone 1) (NQO1), heme oxygenase 1 (HMOX1), FANCD2, helicase, lymphoid specific (HELLS), and cluster of differentiation 44 (CD44). Notably, in addition to participating in ferroptosis, FANCD2 has been confirmed to be associated with various cancers through the FA pathway. It sustains genomic stability by repairing DNA damage[20-22]. Therefore, FANCD2 was chosen for further analysis.

Patient characteristics

The RNA-seq expression data and clinical prognostic information of 1,037 NSCLC patients and 108 normal samples were obtained from the TCGA database. As shown in Table 1, the clinical information of NSCLC patients was summarized, including age, sex, smoking status, pathologic stage (T, N, or M), histologic grade, OS, disease-specific survival (DSS), and the progression-free interval (PFI).

FANCD2 expression is higher in tumor samples than in normal tissues

According to TCGA and GTEx databases, FANCD2 exhibited higher expression in 19 types of tumor tissues than in adjacent normal samples in TCGA database, including LUAD and LUSC (Fig. 2A). Correlation analysis results showed that the FANCD2 mRNA expression was significantly higher with older age (age > 65 years, P< 0.05), in males (P< 0.01), and in smoking patients (P < 0.05), as well as in higher M stage patients (P <0.05, Fig. 2B). Meanwhile, FANCD2 expression had no relation with other clinicopathological characteristics, such as pathological stage, T stage, and N stage (Fig. 2B). In addition, FANCD2 expression exhibited similar tendencies concerning the gender and age of LUSC patients ((P < 0.01 or (P < 0.05), but there were no obvious differences between FANCD2 expression and other variables (P ＞0.05, Fig. 2C). These results indicated the gene and protein expression levels of FANCD2 are significantly higher in LUAD and LUSC tissues.

Similarly, FANCD2 was also significantly elevated in NSCLC tissues compared to levels in normal tissues based on GSE19188, GSE75037, and GSE116959 datasets (all P< 0.001, Fig. 3A). In addition, the IHC results revealed that FANCD2 was overexpressed in LUSC and LUAD tissue in comparison with that in normal tissue according to HPA databases(Fig. 3B).

High FANCD2 mRNA expression is related to short OS in LUAD patients

As shown inFig. 4A,the 20-year OS, DSS, and PFI rates of LUAD patients were remarkably higher with low FANCD2 expression compared to those with high FANCD2 expression (P = 0.04, 0.03, and 0.09 respectively).However, there was no significant difference between FANCD2 expression and OS (P= 0.639), DSS (P= 0.68), and PFI (P= 0.492) in LUSC patients (Fig. 4B).

Univariate analysis showed that higher FANCD2 expression was related to poor OS (HR = 1.495, 95% CI = 1.120–1.994, P< 0.01) in LUAD patients (Fig. 5A).Multivariate Cox regression analysis was used to further explore the relevance of FANCD2 to OS.The results suggested that high FANCD2 expression was an independent predictor of OS (HR = 1.716, 95% CI = 1.195–2.465, P < 0.01, Fig. 5B). Therefore, FANCD2 was considered a risk factor in predicting a worse prognosis.

Genetic alteration and interaction network analyses of FANCD2

The cBioportal online tool was then performed to explore the types and frequencies of FANCD2 alterations in the LUAD patients. Results revealed that FANCD2 was highly conserved (only a 1.4% frequency of genomic alterations, Fig. 6A). Subsequently, the interaction networks showed 20 genes and 10 proteins with the highest relevance to FANCD2 (Fig. 6B). FANCI, FANCL, FAN1, FANCE, FANCC, and USP1 appeared in two networks, for which correlation scores were 0.999, 0.999, 0.999, 0.999, 0.998, and 0.998 respectively in the PPI network. This result implied that these genes and/or proteins could be functional partners of FANCD2 in LUAD.

Functions of FANCD2 in LUAD

To better understand the relevance of FANCD2 expression in LUAD and potential mechanism, single-cell analysis was utilized to explore the associated functional states based on the CancerSEA database. The results suggested that FANCD2 was positively correlated with cell cycle, DNA repair, and proliferation but negatively correlated with angiogenesis, quiescence, inflammation, metastasis, and differentiation (all P < 0.001,Fig. 7A and B). Moreover, overrepresentation enrichment analysis (ORA) illustrated that FANCD2 participated in the interleukin signaling pathway, de novo pyrimidine deoxyribonucleotide biosynthesis, DNA replication, de novo purine biosynthesis, arginine biosynthesis, angiotensin II-stimulated signaling through G proteins and beta-arrestin, the circadian clock system, and the EGF receptor signaling pathway (all P < 0.05, Fig. 7C). Notably, the interleukin signaling pathway had the highest correlation with FANCD2. It is well-known thatinterleukin family members play important roles in the immune response and inflammation.

Association between FANCD2 expression and immune-inhibitory and immune-stimulatory functions

A co-expression study was performed to investigate the relationship between FANCD2 expression and immunomodulators based on TISIDB, including immune inhibitors and immunostimulators. Heatmaps illustrated the association between FANCD2 expression and 12 immunoinhibitors and 28 immunostimulators across 30 tumors (Fig. 8). Among these immunoinhibitors, FANCD2 expression had positive associations withCD274 (Cor = 0.191, p = 1.22 e-05), CTLA4 (Cor = 0.157, p = 3.52 e-04), LAG3 (Cor = (Cor = 0.206, p = 2.47 e-06),and PDCD1 (Cor = 0.137, p = 1.8 e-03) in LUAD (Fig. 8A). Regarding variousimmunostimulators, FANCD2 had negative associations withTNFSF13 (Cor = −0.466, P < 2.2e-16), TMEM173 (Cor = −0.439, P < 2.2 e-16), CD40LG (Cor = −0.26, P = 2.41e-09), HHLA2 (Cor = −0.254, P = 4.97e-09), and IL6R (Cor = −0.288, P = 3.23e-11) (Fig. 8B).

Correlation between FANCD2 expression and infiltrating immune cells

Tumor-infiltrating lymphocytes are associated with the prognosis of patients with multiple cancers[24]. As shown inFig. 9A, FANCD2 expression levels were strongly associated with levels of infiltrating B cells (Cor = −0.157, p = 4.84 e-04), CD8⁺ T cells (Cor = 0.127, p = 4.74 e-03), macrophages (Cor = 0.105, p = 1.97 e-02), and neutrophils (Cor = 0.232, p = 1.82 e-07) in LUAD cases. However, there was no association between FANCD2 expression and tumor purity, DCs, or CD4⁺ T cells. To gain more insight into the relationship between FANCD2 expression and immune infiltration, this study assessed subjects based on 24 types of infiltrating immune cells using the ssGSEA database (Fig. 9B).Specifically, FANCD2 was negatively related to B cells, CD8⁺T cells, DC cells, macrophages, eosinophils, iDC(interdendritic) cells, mast cells, neutrophils, NK CD56 bright cells, pDC (plasmacytoid dendritic) cells, TFH cells, TH17 cells, and NK cells but was positively related to Th2 cells, T helper cells, and Tcm cells (all P < 0.001). Moreover, the heat map showed that most subpopulations among the 24 types of immune cells had moderate to strong relationships (Fig. 9C).

GEPIA and TIMER databases were used to further assess the correlation between FANCD2 and the marker sets of diverse immune cells in LUAD.Table 2 shows that multiple markers of immune cells were significantly related to FANCD2 expression, including Th2 (GATA3), Th9 (TGFBR2), Th17 (IL-21R), Treg (FOXP3, CD25, CCR8), T cell exhaustion (PD-1, CTLA4, LAG3), tumor-associated macrophages (TAMs) (CD80, CCR5), and DCs (CD1C, CD141). These results implied that FANCD2 might affect the function of immune cells by modulating marker gene expression.

LUAD is the most common subtype of lung cancer. Currently, the efficacy of surgery, radiotherapy, chemotherapyand targeted therapy is not satisfying. Notably, ferroptosis is a novel form of cell death, and an increasing body of research has confirmed that it plays a crucial role in anti-tumor treatment, especially in LUAD[15,25]. Moreover, a connection between ferroptosis and cell immunity and cancer immunotherapy has been shown, but the underlying mechanism is not clear[18]. The latest study showed that 76.9% of ferroptosis-related genes are differentially expressed between LUAD tumor tissues and adjacent normal tissues, and some of these DEGs were determined to be remarkably associated with OS[26]. Therefore,ferroptosis-based treatment could be a novel therapeutic strategy for LUAD.

As a nuclear protein, FANCD2 functions in maintaining a stable genome but also participates in the regulation of autophagy[20,21,27]. In addition, FANCD2 has a negative regulatory role in ferroptosis, and tumor cells with low expression of FANCD2 easily undergo ferroptosis. Specifically, FANCD2 deficiency contributes to lipid peroxidation through a decrease in glutathione peroxidase 4 (GPX4), as well as the accumulation of iron through an increase in the expression of transferrin (TF), decreasing ferritin heavy chain 1 (FTH1) and SLC40A1[19].

This study investigated the role of FANCD2 in LUAD progression and prognosis, as well as the relationship with immune cell infiltration(Fig.10).Based on TCGA, GEO, and HPA databases, the results showed that mRNA and protein expression of FANCD2 was upregulated in LUAD samples compared to levels in normal tissues, and its expression was associated with sex, smoking status, and age. KM plots suggested that higher FANCD2 was related to shorter OS, poor DSS, and worse PFI in LUAD patients. Moreover, univariate and multivariate Cox analysis further confirmed that high expression of FANCD2 was an independent adverse prognostic factor, which was consistent with the findings conducted byLei, et al. (28). Therefore, FANCD2 was considered a poor prognostic biomarker for LUAD patients.

FANCD2 is a highly conserved gene, and GeneMANIA, STRING, CancerSEA, and ORA were conducted to further investigate its function. FA family proteins, such as FAN1, FANCI, and FANCE, were confirmed to be crucial genes and proteins that interact with FANCD2 molecules in LUAD through the FA pathway tomaintain genomic stability. For this reason, the efficacy of DNA ICL agents and ionizing radiation are diminished[21]. Beyond this, single-cell analysis illustrated that FANCD2 participates in inflammation, and its intensity decreases with the expression of FANCD2. ORA results showed that FANCD2 participates in the interleukin signaling pathway. Numerous studies have confirmed that factors of the interleukin family, such as IL8, IL10, and IL17, are involved in immune responses and are associated with the outcome of patients[29-31].

Notably, LUAD tends to metastasize at an early stage, which results in a high mortality rate. Further, immune escape is the first crucial step for tumor initiation, progression, and metastasis in TME[32]. Tumor-infiltrating immune cells (TIICs) are an integral part of the TME, playing important roles in suppressing or promoting tumor cell growth. Therefore, remodeling the TME from immune-suppressive to immune-promoting could be a potential anti-tumor therapeutic for LUAD[32]. Recently, studies have shed light on the efficacy of immunotherapy. Immune checkpoint inhibitors targeting T-lymphocyte-associated antigen-4 (CTLA-4), programmed cell death-1 (PD-1), and programmed cell death ligand-1 (PD-L1) have been approved by Food and Drug Administration (FDA).They have been demonstrated to significantly improve the survival of advanced NSCLC patients[33,34].

TISIDB analysis was performed to further explore the mechanism underlying the association between FANCD2 and the TME. Intriguingly, the results revealed that FANCD2 is positively correlated with the expression of CTLA4, PDCD1, and LAG3,whichindicated that FANCD2 could remodelthe immune TME by blockingimmune checkpoints. Moreover, FANCD2 plays an immunosuppressive role by downregulating immunostimulators, such asIL6R, TMEM173, TNFSF13, CD40LG, and HHLA2. Therefore, the results implied that FANCD2 contributes to tumor immune escape by modulating the immunosuppressive microenvironment, which was consistent with previous studies[35,36].

Co-expression analyses indicated that FANCD2 expression was remarkably negatively related to the infiltration ofCD8⁺T cells, NK cells, and DC cells but was positively related to Th2 cells. CD8⁺T cells (Fig.10), as preferred cancer-targeting immunotherapy cells, through exocytosis and the release of perforin-granzyme and activation of caspases via the release of cytochrome c in cancer cells, contribute to tumor cell apoptosis[37]. NK cells are the first line of defense against tumors, and they not only release perforin and granzymes but also excrete various cytokines (IFN-γ, TNF), chemokines (IL-10), or growth factors (GM-CSF) to play a crucial role inantitumor effects and antiviral infection. Notably, IFN-γ enhances the function of antigen-presenting cells, inhibits angiogenesis, induces Th1 cells, and promotes M1 macrophage polarization, which remarkably increases the effect of immune surveillance and immune elimination in the TME[38]. In addition, CD141⁺DCs express lymphotoxin beta transcripts to contribute to lymphocyte recruitment, the priming and proliferation of cytotoxic T cells, and CD1C⁺subpopulations of DCs that promote the maintenance of immune memory[39,40]. In contrast, Th2 cells secrete anti-inflammatory factors such as IL-4, IL-5, IL-6, and IL-10 to weaken the anti-tumor immune response. GATA3, as the genetic marker of Th2 cells, was positively correlated with the FANCD2 level, and this not only promotes Th2 differentiation but also inhibits Th1 differentiation[41]. Hence, the Th2 shift in the TME is considered to promote tumor relapse, metastasis, and poor prognosis[42].

TAMs participate in angiogenesis and lymphangiogenesis, contributing to the progression of NSCLC[43]. However, this study showed that the FANCD2 expression level had no significant association with the infiltration of TAMs, but it could modulate the expression of CD80 and CCR5 to enhance the immunosuppressive function of TAMs. A previous study suggested that CD80 binds to the CTLA-4 receptor, inhibiting T-cell activation[44]. TAMs could independently stimulate tumor cell growth and migration via theCCL5/CCR1/CCR5 axis [45]. These findings revealed that FANCD2 plays a crucial role in recruiting different TIICs and regulating anti-tumor immunity.

In addition, Treg cell markers, such as FOXP3 and CD25, which have a crucial function in suppressing the antitumor immune response[46], were strongly correlated with FANCD2 expression.Several studies have documented that live tumor-infiltrating Tregs are related to poor prognosis in NSCLC patients[47]. However, apoptotic Treg cells also mediate immunosuppression via the adenosine and A2A pathways[48]. Intriguingly,Treg cells that undergo ferroptosis potentiate antitumor immunity, characterized byhigh ratios of cytotoxic CD8⁺ T cells to CD4⁺ T cells in the TME[49]. Therefore, inducing cell ferroptosis of Treg cells is also an antitumor treatment. An additional study demonstrated that activated CD8⁺T cellsrelease IFN-γ to restrain system xc- uptake cystine, promoting tumor cell lipid peroxidation and subsequently contributing to ferroptosis¹⁸. Meanwhile, enhancing ferroptosis has been shown to contribute to the efficacy of antitumorimmunotherapy[18,50]. These results further confirmed that FANCD2 is a crucial molecule for synergetic ferroptosis and immunotherapy.

Inducing cancer cell ferroptosis is considered a promising anti-tumor treatment, especially in combination therapies[51,52]. An increasing number of studies have suggested that TME and tumor biology should be further studied, as they play crucial roles in identifying appropriate treatments for patients or even developing new therapeutic options[25,53-55]. Therefore, FANCD2 might be a powerful factor to predict patient outcomes, and its expression level could be a potential novel standard to screen LUAD patients suitable for ferroptosis therapy and/or immunotherapy.

There are several limitations to this study. First, all of the results were obtained retrospectively; more prospective data and prospective studies are needed to confirm the clinical efficacy. Second, the present study lacked in vitro and in vivo investigations to further explain the mechanisms whereby FANCD2 influences ferroptosis and immunotherapy. More efforts are needed to investigate the potential function of FANCD2 in LUAD.

This study systematically analyzed the role of FANCD2 in tumor progression, prognosis,and therapy for patients with LUAD. These results demonstrated that upregulated FANCD2 contributes to immune escape and is associated with worse outcomes for LUAD patients. This might be associated with FANCD2 participating in maintaining a stable tumor cell genome, protecting cells from ferroptosis, and constructing an immunosuppressive microenvironment. Furthermore, FANCD2 recruits immunosuppressive cells into the TME and regulates the expression of corresponding immune markers to weaken the anti-tumor immune response. Hence, FANCD2 was considered a novel potential bio-target for identifying patients who may benefit from ferroptosis-induction treatment and/or immunotherapy.

Identification of ferroptosis- and NSCLC-related targets

Ferroptosis-related targets were identified from FerrDb (http://www.zhounan.org/ferrdb), which has data on 253 regulators, 111 markers, and 95 ferroptosis-associated diseases. NSCLC-related targets were identified from the GEO database, which contains many bioinformatics datasets from the National Center of Biotechnology Information (https://www.ncbi.nlm.nih.gov/geo/). Three gene expression datasets (GSE75037, GSE19188, and GSE116959) derived from human NSCLC tissues and adjacent normal tissues were included. Genes with an adjusted P< 0.01 and |log₂(fold-change)| >2 were defined as DEGs, which were considered to play essential roles in NSCLC progression and defined as key targets for inducing ferroptosis to treat NSCLC.

Data acquisition and analysis

TCGA (https://portal.gdc.cancer.gov/) isa freely available, large-scale, and publicly open-access cancer genomic database. All transcriptome RNA-seq data (n=1,145)and equivalent clinical data related to NSCLC were downloaded. Based on pathological characteristics, all patients were divided into LUAD (n=516) and LUSC (n=493) groups. Subgroup analysis was performed to investigate the effect of FANCD2 on the pathological stage and outcome. Three independent cohorts including tumor tissues and control samples of NSCLC patients from the GEO databases GSE75037 (n=166), GSE19188 (n=156), and GSE116959 (n = 68) were used to confirm the findings from TCGA datasets. HPA (https://www.proteinatlas.org/) is a comprehensive resource database of the human proteome, and the protein levels of FANCD2 in normal lung tissues and LUAD and LUSC tissues were compared according to immunohistochemistry (IHC) results.

Survival and statistical analyses

Based on the median expression of FANCD2, patients with LUAD and LUSC were divided into high and low groups. To identify whether the FANCD2 expression level influenced LUAD and LUSC patient clinical survival, Kaplan–Meier (KM) survival curves were generated to estimate its prognostic significance.

Univariate and multivariate logistic regression analyses

To further identify the prognostic value of FANCD2 in LUAD, Cox analyses were adopted to evaluate the relationship among different clinical characteristics and prognosis. Univariate Cox analysis was conducted for every variable comparing the expression level of FANCD2 and patient overall survival (OS) in each cohort to confirm their association with LUAD prognosis. Subsequently, multivariate Cox analysis, including all variables, was used to evaluate whether FANCD2 was an independent prognostic factor for LUAD patient outcome.

Genetic alteration and interaction network analyses

Based on cBioPortal (http://cbioportal.org), an open-access multidimensional cancer genomics resource, FANCD2 alterations were analyzed in LUAD samples collected from TCGA. GeneMANIA (http://genemania.org/) and STRING (https://string-preview.org/) are source websites used to construct gene–gene and protein–protein interaction networks, respectively. Both were applied to investigate FANCD2-related genes and proteins.

Single-cell analysis

The CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) database provides a cancer single-cell functional state atlas. This study used CancerSEA to explore the function of FANCD2-regulated genes, as well as the correlation between FANCD2 expression levels and these functions.

TISIDB database analysis

TISIDB (http://cis.hku.hk/TISIDB/), a web portal, is used toprobe tumor-immune interactions, which was applied to evaluate correlations between FANCD2 and immune-suppressive genes and immune-activating genes in this study.

TIMER database analysis

TIMER (https://cistrome.shinyapps.io/timer/) is a web server for the systematic analysis ofsix tumor-infiltrating immune subsets across diverse cancer types. Here, the correlation between FANCD2 expression and the infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, dendritic cells (DCs), macrophages, and neutrophilswas assessed in LUAD patients. Additionally, the association between FANCD2 expression and tumor purity was tested, according to the “Correlation” module of TIMER and the tumor purity-corrected partial.

ssGSEA and CIBERSORT analysis

In total, 24 types of FANCD2-related immune cells were acquired from ssGSEA using the GSVA package in R (4.0.3). Pearson correction analysis was performed to further assess the relevance and enrichment scores between FANCD2 and the different immune cells. CIBERSORT (http://cibersort.stanford.edu/), a deconvolution algorithm based on gene expression, was used to reveal the relevance of 24 types of immune cells in LUAD.

GEPIA database analysis

GEPIA (http://gepia.cancer-pku.cn/index.html) covers thousands of tumors and normal samples from TCGA and the Genotype-Tissue Expression Project (GTEx,http://www.gtexportal.org/home/index.html). It focuses on the analysis of RNA-seq data. The association between FANCD2 levels and multiple markers for various immune cells was evaluated according to the GEPIA database.

Statistical analysis

Student’s t-tests and Wilcoxon tests were performed for assessing differences between two groups, and a Kruskal-Wallis test was used for more than two groups. To evaluate patient survival, KM curves, as well as univariate and multivariate logistic regression analyses, were conducted. Spearmen or Pearson correlations were applied to calculate the relationship between FANCD2 and immune infiltration. Data analyses were based on R (v4.0.3), and P< 0.05 was considered to indicate statistical significance.

FANCD2: Fanconi anemia (FA) group D2; LUAD, lung adenocarcinoma; LUSA, lung squamous cell carcinoma; NSCLC, Non-small cell lung cancer; TME, tumor immune microenvironment; TIICs, tumor-infiltrating immune cells; IHC, immunohistochemistry; OS, overall survival; TAMs, tumor-associated macrophages; DSS, disease-specific survival; PFI, progression-free interval; CTLA-4, T-lymphocyte-associated antigen-4; PD-1, programmed cell death-1; PD-L1, programmed cell death ligand-1; FDA, Food and Drug Administration; DEGs, differentially expressed genes; NQO1, nicotinamide adenine dinucleotide phosphate (NAD[P]H) dehydrogenase (quinone 1); HMOX1, heme oxygenase 1; CD44, cluster of differentiation 44.

Author contributions

Jingtao Zhang wrote the original draft, Fei Xu and Minghao Guo prepared the figures and tables, Dongli Wang and Xiubao Chen analyzed the data, Minmin Yu downloaded the raw data from TCGA and GEO databases, Dexin Zhang reviewed the relevant literature, Weida Chen proofread the manuscript, and Fei Xu edited the draft and made revisions.

Acknowledgement

No.

Funding

The present study was supported by the National Natural Science Foundation of China (grant No. 82004281), the China Postdoctoral Science Foundation (grant No. 2021T140427 and 2021M691986), and the Development Plan of Shandong Medical and Health Technology (grant No. 2019WS581).

Availability of data and materials

All data analyzed in this study were generated from public databases.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors agreed with publication.

Competing interests

The authors declare that they have no conflict of interest.

[1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 71 (3)(2021):209-249.http://doi.org./10.3322/caac.21660. doi:10.3322/caac.21660.

[2] Brahmer JR, Govindan R, Anders RA, Antonia SJ, Sagorsky S, Davies MJ, Dubinett SM, Ferris A, Gandhi L, Garon EB, Hellmann MD, Hirsch FR, Malik S, Neal JW, Papadimitrakopoulou VA, Rimm DL, Schwartz LH, Sepesi B, Yeap BY, Rizvi NA, Herbst RS. The Society for Immunotherapy of Cancer consensus statement on immunotherapy for the treatment of non-small cell lung cancer (NSCLC). J Immunother Cancer. 6 (1)(2018):75.http://doi.org./10.1186/s40425-018-0382-2. doi:10.1186/s40425-018-0382-2.

[3] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA: a cancer journal for clinicians. 69 (1)(2019):7-34.http://doi.org./10.3322/caac.21551. doi:10.3322/caac.21551.

[4] Xu JY, Zhang C, Wang X, Zhai L, Ma Y, Mao Y, Qian K, Sun C, Liu Z, Jiang S, Wang M, Feng L, Zhao L, Liu P, Wang B, Zhao X, Xie H, Yang X, Zhao L, Chang Y, Jia J, Wang X, Zhang Y, Wang Y, Yang Y, Wu Z, Yang L, Liu B, Zhao T, Ren S, Sun A, Zhao Y, Ying W, Wang F, Wang G, Zhang Y, Cheng S, Qin J, Qian X, Wang Y, Li J, He F, Xiao T, Tan M. Integrative Proteomic Characterization of Human Lung Adenocarcinoma. Cell. 182 (1)(2020):245-261.e217.http://doi.org./10.1016/j.cell.2020.05.043. doi:10.1016/j.cell.2020.05.043.

[5] Jia R, Sui Z, Zhang H, Yu Z. Identification and Validation of Immune-Related Gene Signature for Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma. Frontiers in molecular biosciences. 8 (2021):679031.http://doi.org./10.3389/fmolb.2021.679031. doi:10.3389/fmolb.2021.679031.

[6] Li C, Tian C, Liu Y, Liang J, Zeng Y, Yang Q, Liu Y, Wu D, Wu J, Wang J, Zhang K, Gu F, Hu Y, Liu L. Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma. Frontiers in genetics. 12 (2021):619821.http://doi.org./10.3389/fgene.2021.619821. doi:10.3389/fgene.2021.619821.

[7] Deng HY, Zeng M, Li G, Alai G, Luo J, Liu LX, Zhou Q, Lin YD. Lung Adenocarcinoma has a Higher Risk of Lymph Node Metastasis than Squamous Cell Carcinoma: A Propensity Score-Matched Analysis. World journal of surgery. 43 (3)(2019):955-962.http://doi.org./10.1007/s00268-018-4848-7. doi:10.1007/s00268-018-4848-7.

[8] Zhang D, Chen X, Zhu D, Qin C, Dong J, Qiu X, Fan M, Zhuo Q, Tang X. Intrapulmonary lymph node metastasis is common in clinically staged IA adenocarcinoma of the lung. Thorac Cancer. 10 (2)(2019):123-127.http://doi.org./10.1111/1759-7714.12908. doi:10.1111/1759-7714.12908.

[9] Stockwell BR, Friedmann Angeli JP, Bayir H, Bush AI, Conrad M, Dixon SJ, Fulda S, Gascón S, Hatzios SK, Kagan VE, Noel K, Jiang X, Linkermann A, Murphy ME, Overholtzer M, Oyagi A, Pagnussat GC, Park J, Ran Q, Rosenfeld CS, Salnikow K, Tang D, Torti FM, Torti SV, Toyokuni S, Woerpel KA, Zhang DD. Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell. 171 (2)(2017):273-285.http://doi.org./10.1016/j.cell.2017.09.021. doi:10.1016/j.cell.2017.09.021.

[10] Gao M, Jiang X. To eat or not to eat-the metabolic flavor of ferroptosis. Current opinion in cell biology. 51 (2018):58-64.http://doi.org./10.1016/j.ceb.2017.11.001. doi:10.1016/j.ceb.2017.11.001.

[11] Bebber CM, Müller F, Prieto Clemente L, Weber J, von Karstedt S. Ferroptosis in Cancer Cell Biology. Cancers (Basel). 12 (1)(2020).http://doi.org./10.3390/cancers12010164. doi:10.3390/cancers12010164.

[12] Liang C, Zhang X, Yang M, Dong X. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla). 31 (51)(2019):e1904197.http://doi.org./10.1002/adma.201904197. doi:10.1002/adma.201904197.

[13] Hassannia B, Vandenabeele P, Vanden Berghe T. Targeting Ferroptosis to Iron Out Cancer. Cancer cell. 35 (6)(2019):830-849.http://doi.org./10.1016/j.ccell.2019.04.002. doi:10.1016/j.ccell.2019.04.002.

[14] Wang Z, Zhang X, Tian X, Yang Y, Ma L, Wang J, Yu Y. CREB stimulates GPX4 transcription to inhibit ferroptosis in lung adenocarcinoma. Oncology reports. 45 (6)(2021).http://doi.org./10.3892/or.2021.8039. doi:10.3892/or.2021.8039.

[15] Ma L, Zhang X, Yu K, Xu X, Chen T, Shi Y, Wang Y, Qiu S, Guo S, Cui J, Miao Y, Tian X, Du L, Yu Y, Xia J, Wang J. Targeting SLC3A2 subunit of system X(C)(-) is essential for m(6)A reader YTHDC2 to be an endogenous ferroptosis inducer in lung adenocarcinoma. Free radical biology & medicine. 168 (2021):25-43.http://doi.org./10.1016/j.freeradbiomed.2021.03.023. doi:10.1016/j.freeradbiomed.2021.03.023.

[16] Zhang Y, Li S, Li F, Lv C, Yang QK. High-fat diet impairs ferroptosis and promotes cancer invasiveness via downregulating tumor suppressor ACSL4 in lung adenocarcinoma. Biology direct. 16 (1)(2021):10.http://doi.org./10.1186/s13062-021-00294-7. doi:10.1186/s13062-021-00294-7.

[17] Klöditz K, Fadeel B. Three cell deaths and a funeral: macrophage clearance of cells undergoing distinct modes of cell death. Cell death discovery. 5 (2019):65.http://doi.org./10.1038/s41420-019-0146-x. doi:10.1038/s41420-019-0146-x.

[18] Wang W, Green M, Choi JE, Gijón M, Kennedy PD, Johnson JK, Liao P, Lang X, Kryczek I, Sell A, Xia H, Zhou J, Li G, Li J, Li W, Wei S, Vatan L, Zhang H, Szeliga W, Gu W, Liu R, Lawrence TS, Lamb C, Tanno Y, Cieslik M, Stone E, Georgiou G, Chan TA, Chinnaiyan A, Zou W. CD8(+) T cells regulate tumour ferroptosis during cancer immunotherapy. Nature. 569 (7755)(2019):270-274.http://doi.org./10.1038/s41586-019-1170-y. doi:10.1038/s41586-019-1170-y.

[19] Song X, Xie Y, Kang R, Hou W, Sun X, Epperly MW, Greenberger JS, Tang D. FANCD2 protects against bone marrow injury from ferroptosis. Biochemical and biophysical research communications. 480 (3)(2016):443-449.http://doi.org./10.1016/j.bbrc.2016.10.068. doi:10.1016/j.bbrc.2016.10.068.

[20] Yang SY, Hsiung CN, Li YJ, Chang GC, Tsai YH, Chen KY, Huang MS, Su WC, Chen YM, Hsiung CA, Yang PC, Chen CJ, Wu PE, Yu JC, Shen CY, Hsu HM. Fanconi anemia genes in lung adenocarcinoma- a pathway-wide study on cancer susceptibility. Journal of biomedical science. 23 (2016):23.http://doi.org./10.1186/s12929-016-0240-9. doi:10.1186/s12929-016-0240-9.

[21] Wang GZ, Liu YQ, Cheng X, Zhou GB. Celastrol induces proteasomal degradation of FANCD2 to sensitize lung cancer cells to DNA crosslinking agents. Cancer Sci. 106 (7)(2015):902-908.http://doi.org./10.1111/cas.12679. doi:10.1111/cas.12679.

[22] Li X, Liu J, Wang K, Zhou J, Zhang H, Zhang M, Shi Y. Polymorphisms and rare variants identified by next-generation sequencing confer risk for lung cancer in han Chinese population. Pathology, research and practice. 216 (4)(2020):152873.http://doi.org./10.1016/j.prp.2020.152873. doi:10.1016/j.prp.2020.152873.

[23] Li C, Zhang Y, Liu J, Kang R, Klionsky DJ, Tang D. Mitochondrial DNA stress triggers autophagy-dependent ferroptotic death. Autophagy. 17 (4)(2021):948-960.http://doi.org./10.1080/15548627.2020.1739447. doi:10.1080/15548627.2020.1739447.

[24] Salgado R, Loi S. Tumour infiltrating lymphocytes in breast cancer: increasing clinical relevance. The Lancet Oncology. 19 (1)(2018):3-5.http://doi.org./10.1016/s1470-2045(17)30905-1. doi:10.1016/s1470-2045(17)30905-1.

[25] Zhang X, Yu K, Ma L, Qian Z, Tian X, Miao Y, Niu Y, Xu X, Guo S, Yang Y, Wang Z, Xue X, Gu C, Fang W, Sun J, Yu Y, Wang J. Endogenous glutamate determines ferroptosis sensitivity via ADCY10-dependent YAP suppression in lung adenocarcinoma. Theranostics. 11 (12)(2021):5650-5674.http://doi.org./10.7150/thno.55482. doi:10.7150/thno.55482.

[26] Gao X, Tang M, Tian S, Li J, Liu W. A ferroptosis-related gene signature predicts overall survival in patients with lung adenocarcinoma. Future Oncol. 17 (12)(2021):1533-1544.http://doi.org./10.2217/fon-2020-1113. doi:10.2217/fon-2020-1113.

[27] Sertorio M, Amarachintha S, Wilson A, Pang Q. Fancd2 Deficiency Impairs Autophagy Via Deregulating The Ampk/Foxo3a/Akt Pathway %J Blood. 122 (21)(2013):3713-3713.

[28] Lei LC, Yu VZ, Ko JMY, Ning L, Lung ML. FANCD2 Confers a Malignant Phenotype in Esophageal Squamous Cell Carcinoma by Regulating Cell Cycle Progression. Cancers (Basel). 12 (9)(2020).http://doi.org./10.3390/cancers12092545. doi:10.3390/cancers12092545.

[29] Schalper KA, Carleton M, Zhou M, Chen T, Feng Y, Huang SP, Walsh AM, Baxi V, Pandya D, Baradet T, Locke D, Wu Q, Reilly TP, Phillips P, Nagineni V, Gianino N, Gu J, Zhao H, Perez-Gracia JL, Sanmamed MF, Melero I. Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nature medicine. 26 (5)(2020):688-692.http://doi.org./10.1038/s41591-020-0856-x. doi:10.1038/s41591-020-0856-x.

[30] Zhang H, Li R, Cao Y, Gu Y, Lin C, Liu X, Lv K, He X, Fang H, Jin K, Fei Y, Chen Y, Wang J, Liu H, Li H, Zhang H, He H, Zhang W. Poor Clinical Outcomes and Immunoevasive Contexture in Intratumoral IL-10-Producing Macrophages Enriched Gastric Cancer Patients. Annals of surgery.(2020).http://doi.org./10.1097/sla.0000000000004037. doi:10.1097/sla.0000000000004037.

[31] Zhang Y, Chandra V, Riquelme Sanchez E, Dutta P, Quesada PR, Rakoski A, Zoltan M, Arora N, Baydogan S, Horne W, Burks J, Xu H, Hussain P, Wang H, Gupta S, Maitra A, Bailey JM, Moghaddam SJ, Banerjee S, Sahin I, Bhattacharya P, McAllister F. Interleukin-17-induced neutrophil extracellular traps mediate resistance to checkpoint blockade in pancreatic cancer. The Journal of experimental medicine. 217 (12)(2020).http://doi.org./10.1084/jem.20190354. doi:10.1084/jem.20190354.

[32] Li J, Wang S, Wang N, Zheng Y, Yang B, Wang X, Zhang J, Pan B, Wang Z. Aiduqing formula inhibits breast cancer metastasis by suppressing TAM/CXCL1-induced Treg differentiation and infiltration. Cell communication and signaling : CCS. 19 (1)(2021):89.http://doi.org./10.1186/s12964-021-00775-2. doi:10.1186/s12964-021-00775-2.

[33] Borghaei H, Gettinger S, Vokes EE, Chow LQM, Burgio MA, de Castro Carpeno J, Pluzanski A, Arrieta O, Frontera OA, Chiari R, Butts C, Wójcik-Tomaszewska J, Coudert B, Garassino MC, Ready N, Felip E, García MA, Waterhouse D, Domine M, Barlesi F, Antonia S, Wohlleber M, Gerber DE, Czyzewicz G, Spigel DR, Crino L, Eberhardt WEE, Li A, Marimuthu S, Brahmer J. Five-Year Outcomes From the Randomized, Phase III Trials CheckMate 017 and 057: Nivolumab Versus Docetaxel in Previously Treated Non-Small-Cell Lung Cancer. J Clin Oncol. 39 (7)(2021):723-733.http://doi.org./10.1200/jco.20.01605. doi:10.1200/jco.20.01605.

[34] Chalmers AW, Patel S, Boucher K, Cannon L, Esplin M, Luckart J, Graves N, Van Duren T, Akerley W. Phase I Trial of Targeted EGFR or ALK Therapy with Ipilimumab in Metastatic NSCLC with Long-Term Follow-Up. Target Oncol. 14 (4)(2019):417-421.http://doi.org./10.1007/s11523-019-00658-0. doi:10.1007/s11523-019-00658-0.

[35] Hong Y, Lin M, Ou D, Huang Z, Shen P. A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma. BMC cancer. 21 (1)(2021):831.http://doi.org./10.1186/s12885-021-08559-0. doi:10.1186/s12885-021-08559-0.

[36] Yang L, Li C, Qin Y, Zhang G, Zhao B, Wang Z, Huang Y, Yang Y. A Novel Prognostic Model Based on Ferroptosis-Related Gene Signature for Bladder Cancer. Front Oncol. 11 (2021):686044.http://doi.org./10.3389/fonc.2021.686044. doi:10.3389/fonc.2021.686044.

[37] Farhood B, Najafi M, Mortezaee K. CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: A review. J Cell Physiol. 234 (6)(2019):8509-8521.http://doi.org./10.1002/jcp.27782. doi:10.1002/jcp.27782.

[38] Morvan MG, Lanier LL. NK cells and cancer: you can teach innate cells new tricks. Nature reviews Cancer. 16 (1)(2016):7-19.http://doi.org./10.1038/nrc.2015.5. doi:10.1038/nrc.2015.5.

[39] Lavin Y, Kobayashi S, Leader A, Amir ED, Elefant N, Bigenwald C, Remark R, Sweeney R, Becker CD, Levine JH, Meinhof K, Chow A, Kim-Shulze S, Wolf A, Medaglia C, Li H, Rytlewski JA, Emerson RO, Solovyov A, Greenbaum BD, Sanders C, Vignali M, Beasley MB, Flores R, Gnjatic S, Pe'er D, Rahman A, Amit I, Merad M. Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell. 169 (4)(2017):750-765.e717.http://doi.org./10.1016/j.cell.2017.04.014. doi:10.1016/j.cell.2017.04.014.

[40] Nizzoli G, Larghi P, Paroni M, Crosti MC, Moro M, Neddermann P, Caprioli F, Pagani M, De Francesco R, Abrignani S, Geginat J. IL-10 promotes homeostatic proliferation of human CD8(+) memory T cells and, when produced by CD1c(+) DCs, shapes naive CD8(+) T-cell priming. European journal of immunology. 46 (7)(2016):1622-1632.http://doi.org./10.1002/eji.201546136. doi:10.1002/eji.201546136.

[41] Yagi R, Zhu J, Paul WE. An updated view on transcription factor GATA3-mediated regulation of Th1 and Th2 cell differentiation. International immunology. 23 (7)(2011):415-420.http://doi.org./10.1093/intimm/dxr029. doi:10.1093/intimm/dxr029.

[42] Liu XS, Lin XK, Mei Y, Ahmad S, Yan CX, Jin HL, Yu H, Chen C, Lin CZ, Yu JR. Regulatory T Cells Promote Overexpression of Lgr5 on Gastric Cancer Cells via TGF-beta1 and Confer Poor Prognosis in Gastric Cancer. Frontiers in immunology. 10 (2019):1741.http://doi.org./10.3389/fimmu.2019.01741. doi:10.3389/fimmu.2019.01741.

[43] Hwang I, Kim JW, Ylaya K, Chung EJ, Kitano H, Perry C, Hanaoka J, Fukuoka J, Chung JY, Hewitt SM. Tumor-associated macrophage, angiogenesis and lymphangiogenesis markers predict prognosis of non-small cell lung cancer patients. Journal of translational medicine. 18 (1)(2020):443.http://doi.org./10.1186/s12967-020-02618-z. doi:10.1186/s12967-020-02618-z.

[44] Chikuma S. CTLA-4, an Essential Immune-Checkpoint for T-Cell Activation. Current topics in microbiology and immunology. 410 (2017):99-126.http://doi.org./10.1007/82_2017_61. doi:10.1007/82_2017_61.

[45] Pham K, Huynh D, Le L, Delitto D, Yang L, Huang J, Kang Y, Steinberg MB, Li J, Zhang L, Liu D, Tang MS, Liu C, Wang H. E-cigarette promotes breast carcinoma progression and lung metastasis: Macrophage-tumor cells crosstalk and the role of CCL5 and VCAM-1. Cancer Lett. 491 (2020):132-145.http://doi.org./10.1016/j.canlet.2020.08.010. doi:10.1016/j.canlet.2020.08.010.

[46] Litwin TR, Irvin SR, Chornock RL, Sahasrabuddhe VV, Stanley M, Wentzensen N. Infiltrating T-cell markers in cervical carcinogenesis: a systematic review and meta-analysis. Br J Cancer. 124 (4)(2021):831-841.http://doi.org./10.1038/s41416-020-01184-x. doi:10.1038/s41416-020-01184-x.

[47] Shimizu K, Nakata M, Hirami Y, Yukawa T, Maeda A, Tanemoto K. Tumor-infiltrating Foxp3+ regulatory T cells are correlated with cyclooxygenase-2 expression and are associated with recurrence in resected non-small cell lung cancer. J Thorac Oncol. 5 (5)(2010):585-590.http://doi.org./10.1097/JTO.0b013e3181d60fd7. doi:10.1097/JTO.0b013e3181d60fd7.

[48] Maj T, Wang W, Crespo J, Zhang H, Wang W, Wei S, Zhao L, Vatan L, Shao I, Szeliga W, Lyssiotis C, Liu JR, Kryczek I, Zou W. Oxidative stress controls regulatory T cell apoptosis and suppressor activity and PD-L1-blockade resistance in tumor. Nature immunology. 18 (12)(2017):1332-1341.http://doi.org./10.1038/ni.3868. doi:10.1038/ni.3868.

[49] Xu C, Sun S, Johnson T, Qi R, Zhang S, Zhang J, Yang K. The glutathione peroxidase Gpx4 prevents lipid peroxidation and ferroptosis to sustain Treg cell activation and suppression of antitumor immunity. Cell reports. 35 (11)(2021):109235.http://doi.org./10.1016/j.celrep.2021.109235. doi:10.1016/j.celrep.2021.109235.

[50] Lang X, Green MD, Wang W, Yu J, Choi JE, Jiang L, Liao P, Zhou J, Zhang Q, Dow A, Saripalli AL, Kryczek I, Wei S, Szeliga W, Vatan L, Stone EM, Georgiou G, Cieslik M, Wahl DR, Morgan MA, Chinnaiyan AM, Lawrence TS, Zou W. Radiotherapy and Immunotherapy Promote Tumoral Lipid Oxidation and Ferroptosis via Synergistic Repression of SLC7A11. Cancer discovery. 9 (12)(2019):1673-1685.http://doi.org./10.1158/2159-8290.Cd-19-0338. doi:10.1158/2159-8290.Cd-19-0338.

[51] Zhang X, Sui S, Wang L, Li H, Zhang L, Xu S, Zheng X. Inhibition of tumor propellant glutathione peroxidase 4 induces ferroptosis in cancer cells and enhances anticancer effect of cisplatin. J Cell Physiol. 235 (4)(2020):3425-3437.http://doi.org./10.1002/jcp.29232. doi:10.1002/jcp.29232.

[52] Lei G, Mao C, Yan Y, Zhuang L, Gan B. Ferroptosis, radiotherapy, and combination therapeutic strategies. Protein & cell.(2021).http://doi.org./10.1007/s13238-021-00841-y. doi:10.1007/s13238-021-00841-y.

[53] Hu S, Sechi M, Singh PK, Dai L, McCann S, Sun D, Ljungman M, Neamati N. A Novel Redox Modulator Induces a GPX4-Mediated Cell Death That Is Dependent on Iron and Reactive Oxygen Species. Journal of medicinal chemistry. 63 (17)(2020):9838-9855.http://doi.org./10.1021/acs.jmedchem.0c01016. doi:10.1021/acs.jmedchem.0c01016.

[54] Hong T, Lei G, Chen X, Li H, Zhang X, Wu N, Zhao Y, Zhang Y, Wang J. PARP inhibition promotes ferroptosis via repressing SLC7A11 and synergizes with ferroptosis inducers in BRCA-proficient ovarian cancer. Redox biology. 42 (2021):101928.http://doi.org./10.1016/j.redox.2021.101928. doi:10.1016/j.redox.2021.101928.

[55] Lei G, Zhang Y, Hong T, Zhang X, Liu X, Mao C, Yan Y, Koppula P, Cheng W, Sood AK, Liu J, Gan B. Ferroptosis as a mechanism to mediate p53 function in tumor radiosensitivity. Oncogene. 40 (20)(2021):3533-3547.http://doi.org./10.1038/s41388-021-01790-w. doi:10.1038/s41388-021-01790-w.

Table 1. Clinical characteristics of LUAD and LUSC patients based on the TCGA database

Clinical factors		LUAD			LUSC
		Low	High	P value	Low	High	P value
Age, n (%)	Median (IQR)	67 (59, 74)	64 (59, 72)	0.036	69 (63, 74)	67 (60, 73)	0.033
	<=65	115(22.3%)	140(27.1%)	0.043	87 (17.6%)	104(21.1%)	0.170
	>65	142(27.5%)	119(23.1%)		158 (32%)	144(29.2%)
Gender, n (%)	Female	158(29.5%)	128(23.9%)		79 (15.7%)	52 (10.4%)	0.008
Gender, n (%)	Male	109(20.4%)	140(26.2%)		172(34.3%)	199(39.6%)
Smoker, n (%)	No	47 (9%)	28 (5.4%)	0.018	8 (1.6%)	10 (2%)	0.797
Smoker, n (%)	Yes	210(40.3%)	236(45.3%)		238(48.6%)	234(47.8%)
T stage, n (%)	T1	98 (18.4%)	77 (14.5%)	0.031	61 (12.2%)	53 (10.6%)	0.416
	T2	127(23.9%)	162(30.5%)		138(27.5%)	156(31.1%)
	T3	29 (5.5%)	20 (3.8%)		40 (8%)	31 (6.2%)
	T4	11 (2.1%)	8 (1.5%)		12 (2.4%)	11 (2.2%)
N stage, n (%)	N0	175(33.7%)	173(33.3%)	0.580	167(33.7%)	153(30.8%)	0.321
	N1	44 (8.5%)	51 (9.8%)		58 (11.7%)	73 (14.7%)
	N2	38 (7.3%)	36 (6.9%)		17 (3.4%)	23 (4.6%)
	N3	0 (0%)	2 (0.4%)		3 (0.6%)	2 (0.4%)
M stage, n (%)	M0	187(48.4%)	174(45.1%)	0.088	197 (47%)	215(51.3%)	0.270
M stage, n (%)	M1	8 (2.1%)	17 (4.4%)		5 (1.2%)	2 (0.5%)
Pathologic stage, n (%)	Stage I	147(27.9%)	147(27.9%)	0.455	128 (25.7%)	117 (23.5%)	0.491
	Stage II	61 (11.6%)	62 (11.8%)		76 (15.3%)	86 (17.3%)
	Stage III	44 (8.3%)	40 (7.6%)		41 (8.2%)	43 (8.6%)
	Stage IV	9 (1.7%)	17 (3.2%)		5 (1%)	2 (0.4%)
OS event, n (%)	Alive	180(33.6%)	163(30.5%)	0.134	144(28.7%)	142(28.3%)	0.928
OS event, n (%)	Dead	87 (16.3%)	105(19.6%)		107(21.3%)	109(21.7%)	0.777
DSS event, n (%)	Alive	198(39.7%)	181(36.3%)	0.079	183(40.7%)	178(39.6%)
DSS event, n (%)	Dead	51 (10.2%)	69 (13.8%)		43 (9.6%)	46(10.2%)
PFI event, n (%)	Alive	162(30.3%)	147(27.5%)		177(35.3%)	177(35.3%)	1.000
PFI event, n (%)	Dead	105(19.6%)	121(22.6%)		74 (14.7%)	74 (14.7%)

Table 2. Correlation between FANCD2 levels and markers of immune cells based on TIMER and GEPIA databases

Cell type	Gene marker	None		Purity		Tumor		Normal
Cell type	Gene marker	Cor	P	Cor	P	R	P	R	P
B cell	CD19	-0.003	0.9410	0.004	0.9210	-0.085	0.063	0.41	0.0011^**
	CD20(KRT20)	0.027	0.5440	0.021	0.6440	0.071	0.12	-0.039	0.77
	CD38	0.049	0.2720	0.064	0.1560	-0.0043	0.92	0.14	0.29
CD8⁺ T cell	CD8A	0.201	^***	0.231	^***	0.078	0.085	0.11	0.41
	CD8B	0.192	^***	0.202	^***	0.12	0.0082^**	0.085	0.52
	BCL6	0.019	0.6600	0.024	0.5960	0.056	0.22	0.13	0.31
	ICOS	0.162	0.0002^***	0.202	^***	0.069	0.13	0.32	0.014^**
	CXCR5	-0.013	0.7760	-0.005	0.9150	-0.098	0.032	0.4	0.0017^**
Th1	T-bet (TBX21)	0.156	0.0004^***	0.184	^***	0.035	0.44	-0.033	0.8
	STAT4	0.081	0.0646	0.098	0.0291	0.033	0.47	0.31	0.18
	IL12RB2	0.457	^***	0.483	^***	0.22	^***	0.14	0.29
	WSX1(IL27RA)	-0.047	0.2900	-0.043	0.3410	-0.058	0.21	0.24	0.071
	STAT1	0.44	^***	0.472	^***	0.3	^***	0.12	0.36
	IFN-γ(IFNG)	0.311	^***	0.337	^***	0.19	0.000026^***	0.075	0.57
	TNF-α(TNF)	0.103	0.0194^*	0.124	0.0056^**	0.055	0.23	0.33	0.011^*
Th2	GATA3	0.174	0.0001^***	0.204	^***	0.19	0.000026^***	0.03	0.82
	CCR3	-0.02	0.6430	0.001	0.9890	-0.015	0.74	0.11	0.41
	STAT6	-0.07	0.1120	-0.077	0.0858	-0.028	0.55	0.27	0.042^*
	STAT5A	0.055	0.2140	0.076	0.0925	-0.0088	0.85	0.44	0.00056^**
Th9	TGFBR2	-0.13	0.0032^**	-0.123	0.0064^***	-0.12	0.0077^***	-0.19	0.14
	IRF4	0.062	0.1620	0.077	0.0858	-0.068	0.13	0.44	0.00053^**
	PU.1(SPI1)	-0.039	0.3770	-0.038	0.4040	-0.11	0.015^*	0.24	0.071
Th17	STAT3	0.023	0.5980	0.021	0.6350	0.021	0.64	0.12	0.38
	IL-21R	0.149	0.0007 ^***	0.188	^***	0.037	0.41	0.26	0.05
	IL-23R	0.002	0.9580	0.006	0.9010	-0.033	0.47	0.11	0.39
	IL-17A	0.089	0.0436^*	0.089	0.0494	0.073	0.11	0.17	0.19
Th22	CCR10	0.094	0.0321^*	0.08	0.0774	0.11	0.013^*	-0.086	0.52
	AHR	-0.014	0.7550	-0.014	0.7500	-0.012	0.79	0.003	0.98
Treg	FOXP3	0.157	0.0004^***	0.183	***	0.04	0.38	0.42	0.001^**
	CD25(IL2RA)	0.24	^***	0.271	***	0.15	0.00068	0.11	0.39
	CCR8	0.183	^***	0.214	***	0.064	0.16	0.21	0.11
T cell exhaustion	PD-1(PDCD1)	0.219	^***	0.259	***	0.083	0.068	0.38	0.0029^**
	CTLA4	0.24	^***	0.29	***	0.14	0.0029	0.27	0.04
	LAG3	0.265	^***	0.286	***	0.12	0.0086	0.43	0.00071^***
	TIM-3(HAVCR2)	0.074	0.0951	0.088	0.0516	-0.033	0.46	0.097	0.46
Macrophage	CD68	0.034	0.4350	0.044	0.3340	-0.032	0.49	0.15	0.26
	CD11b(ITGAM)	0.006	0.9000	0.019	0.6670	-0.026	0.57	0.23	0.074
M1	INOS(NOS2)	0.042	0.3420	0.029	0.5260	0.048	0.29	-0.25	0.058
	IRF5	0.125	0.0045^**	0.134	0.0028	0.087	0.057	0.4	0.0016^**
	COX2(PTGS2)	0.016	0.7150	0	0.9920	0.02	0.66	-0.048	0.72
M2	CD16	0.042	0.3440	0.055	0.2230	-0.0086	0.85	0.065	0.62
	ARG1	-0.001	0.9830	-0.011	0.8150	0.059	0.19	-0.038	0.77
	MRC1	-0.083	0.0588	-0.079	0.0786	-0.075	0.1	0.38	0.0031^**
	MS4A4A	-0.001	0.9860	0.007	0.8760	-0.083	0.069	0.25	0.06
TAM	CCL2	0.074	0.0921	0.081	0.0739	0.0014	0.97	-0.015	0.91
	CD80	0.129	0.0033^**	0.153	0.0006	0.079	0.082	0.15	0.25
	CD86	0.078	0.0755	0.095	0.0355	-0.028	0.54	0.22	0.1
	CCR5	0.14	0.0014	0.177	0.0001	0.056	0.22	0.27	0.04^*
Monocyte	CD14	0.041	0.3530	0.058	0.1960	-0.06	0.19	-0.12	0.35
	CD16(FCGR3B)	0.042	0.3440	0.055	0.2230	-.0086	0.85	0.065	0.62
	CD115(CSF1R)	0.011	0.8050	0.026	0.5690	-0.047	0.3	0.34	0.00088^**
Neutrophil	CD66b(CEACAM8)	-0.23	^***	-0.229	***	-0.1	0.025	-0.013	0.92
	CD15(FUT4)	0.058	0.1890	0.047	0.2940	0.15	0.0013	0.15	0.26
	CD11b(ITGAM)	0.006	0.9000	0.019	0.6670	-0.026	0.57	0.23	0.074
Natural killer cell	XCL1	0.225	^***	0.261	***	0.2	0.0000058	-0.17	0.19
	CD7	0.191	^***	0.219	***	0.063	0.17	0.16	0.22
	KIR3DL1	0.035	0.4270	0.042	0.3560	0.00052	0.99	-0.19	0.16
Dendritic cell	CD1C(BDCA-1)	-0.339	^***	-0.344	***	-0.23	0.00000028	0.11	0.43
	CD141(THBD)	-0.183	^***	-0.188	***	-0.075	0.1	-0.12	0.38
	CD11c	0.133	0.0026^**	0.154	0.0006	0.011	0.81	0.35	0.0071^**

None: correlation without adjustment, Purity: correlation adjusted by purity, Cor, correlation coefficient.^*P< 0.05, ^**P< 0.01, and ^***P < 0.001.

No competing interests reported.

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Upregulation of Ferroptosis-Related Fanconi Anemia Group D2 is a Poor Prognostic Factor and Indicator of Tumor Immune Cell Infiltration in Lung Adenocarcinoma

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