Comprehensive Analysis of the Autophagy-dependent Ferroptosis-related Gene FANCD2 in Lung Adenocarcinoma

doi:10.21203/rs.3.rs-541121/v2

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Comprehensive Analysis of the Autophagy-dependent Ferroptosis-related Gene FANCD2 in Lung Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-541121/v2

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Background. The development of lung adenocarcinoma (LUAD) involves the interactions between cell proliferation and death. Autophagy-dependent ferroptosis, a distinctive cell death process was implicated in a multitude of diseases, whereas no research revealing the relationship between autophagy-dependent ferroptosis and LUAD pathogenesis was reported. Thus, the primary objective was to explore the role and potential function of the autophagy-dependent ferroptosis-related genes in LUAD.

Methods. Clinical information and transcriptome profiling of patients with LUAD were retrieved and downloaded from open-source databases. Autophagy-dependent ferroptosis-related genes were screened by published articles. The key gene was identified as the intersection between the differentially expressed genes and prognosis-related genes. Patients were divided into high- and low-risk groups using the expression level of the key gene. The validity of the key gene prognosis model was verified by survival analysis. The correlation between the clinical characteristics of LUAD and the expression level of the key gene was analyzed to explore the clinical significance and prognosis value. And the roles of the key gene in response to chemotherapy, immune microenvironment and tumor mutation burden were predicted. The validation of key gene expression levels were further performed by quantitative real-time PCR and immunohistochemistry staining.

Results. FANCD2, a key autophagy-dependent ferroptosis-related gene by searching database, was confirmed as an independent prognostic factor for LUAD occurrence. The high expression level of FANCD2 was associated with an advantaged TNM stage, a less chemotherapy sensitivity, a low ImmuneScore, which indicated a deactivation status in immune microenvironment, a high tumor mutation burden, and a poor survival for LUAD patients. Pathway enrichment analysis showed that FANCD2 were involved in response to oxidative stress and neutrophil mediated immunity. Quantitative real-time PCR and immunohistochemistry staining showed that the expression level of FANCD2 is higher in LUAD patients than normal tissue samples, which was in accordance with the database report.

Conclusion. FANCD2, a key gene related to autophagy-dependent ferroptosis, could work as a biomarker, which predicts the survival, chemotherapy sensitivity, tumor immunity and mutation burden of LUAD. Research of autophagy-dependent ferroptosis and targeting the FANCD2 may offer a new perspective for the treatment and improvement of prognosis in LUAD.

Cancer Biology

lung adenocarcinoma

autophagy-dependent ferroptosis

FANCD2

prognosis

immunity

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors in the world, demonstrating a rising trend in recent years [1]. Traditional treatments, such as surgery, radiotherapy, and chemotherapy, could not meet all LUAD patients’ needs due to the high recurrence and metastasis. Although immunotherapy has been shown to improve survival in LUAD patients, the 5-year overall survival rate is only 23% [2]. The pathogenic mechanism of LUAD should be further elucidated to discover a new effective treatment strategy.

The tumor heterogeneity, including immune microenvironment and tumor mutation burden, could affect the immunotherapy effectiveness. The recent research has revealed that ferroptosis is also involved in T cell immunity and cancer immunotherapy. The increased ferroptosis contributes to the anti-tumour efficacy of immunotherapy [3].

Ferroptosis is an iron-dependent form of regulated cell death which is characterized by the excess reactive oxygen species (ROS) generation and lethal accumulation of lipid peroxidation [4] [5] [6]. Disregulation of ferroptosis has been implicated in multiple physiological and pathological processes, including cancer cell death and T-cell immunity [7]. Autophagy-dependent ferroptosis is a key type of ferroptosis which is featured by excessive autophagy and lysosome activity [8]. The influence of ferroptosis, especially autophagy-dependent ferroptosis, on tumor microenvironment needs to be further studied.

The iron metabolism and homeostasis could be influenced by immune cells and related molecules [9]. Immune cells in the microenvironment play crucial roles in the maintenance of iron metabolism balance [10]. The excessive activation of ferroptosis in tumor cells can lead to tumor antigens exposure which activates the immnue system. Then, the immunogenicity of the microenvironment was improved and the effectiveness of immunotherapy was enhanced [11]. Immunotherapy can activate CD8 + T cells to enhance the lipid peroxidation in tumor cells, which further contribute to the increase in ferroptosis in turn [3]. Therefore, targeting ferroptosis to improve the effectiveness of cancer immunotherapy might become a prospective strategy in the future. In the clinical applications of immunotherapy, tumor mutation burden (TMB) is emphasized as an emerging feature and a biomarker of immunotherapy response [12] [13]. TMB is defined as the total number of somatic, coding, base substitution, and indel mutations per megabase of genome examined [14]. Each of these mutations results in the generation of one protein that is an new antigen and could be recognized by the immune system [15]. Highly mutated tumors are more likely to carry neoantigens, making them become the targets for activated immune cells [14].

In this study, we comprehensively analyze the genome of LUAD, identify autophagy-dependent ferroptosis-related genes closely associated with the prognosis and chemotherapy sensitivity, further construct and validate the predictive model of the key gene, and explore the relationship with immune infiltration and tumor mutation. Our findings may help generate personalized treatment and improve the clinical outcomes of LUAD patients.

Workflow

A multi-step approach was used to identify and analyse the autophagy-dependent ferroptosis-related key gene in LUAD. The transcriptome and clinical information were downloaded from The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO) data. Autophagy-dependent ferroptosis-related genes were screened by the published articles. Differentially expressed genes (DEGs) related to autophagy-dependent ferroptosis were identified. Univariate and multivariate Cox analyses were applied to screen out the independent prognosis genes related to overall survival (OS). The key gene was identified by the intersection of the DEGs and the prognostic genes. The LUAD patients were classified into the high-risk and low-risk groups based on the key gene expression level. Kaplan-Meier (K-M) analysis and receiver operating characteristic (ROC) curve were conducted to analyze the survival prognosis of patients in TCGA and GEO cohort. Chemotherapy sensitivity was predicted between different risk group. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were conducted to investigate the potential bio-function of the key gene. ImmuneScore was calculated using the Tumor Immune Estimation Resource (TIMER) algorithm and the TMB was counted as the total number of mutations per megabyte of tumor tissue.

LUAD patients dataset processing

All the RNA-Seq data was normalized as fragments per kilobase of transcript per million mapped reads. mRNAs ensemble gene identities were derived from the HUGO Gene Nomenclature Committee (HGNC) database. The corresponding clinical information include age, gender, tumor grade, lymph node metastasis, AJCC TNM stages and survival outcomes. Patients with insufficient clinical data were excluded. OS was estimated as the primary endpoint.

Construction and validation of an autophagy-dependent ferroptosis-related gene signature

Autophagy-dependent ferroptosis-related genes were retrieved from the literatures published before January 2021. The gene expression file was obtained after combining the related mRNA expression and the clinical data. The DEGs between tumor and normal tissues were identified with a false discovery rate (FDR) < 0.05 in the TCGA cohort. Univariate and multivariate Cox analysis of OS were performed to screen the genes with prognostic values. The key gene was identified by the intersection of the DEGs and the prognostic genes in the TCGA cohort. Patients were stratified into high-risk and low-risk groups based on the median value of the key gene expression. And the prognostic value was validated by a GEO cohort (GSE116959). The clinical correlation analysis of the key gene was conducted between high- and low-risk groups.The time-dependent ROC curve analyses were conduct to evaluate the predictive power of the key gene. The mRNA expression level of the key gene in various types of cancers were identified in the Oncomine database [16]. The mRNA and protein expression of the key gene in LUAD were determined using the Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas (HPA) database [17] [18].

Chemotherapeutic response prediction

The commonly used chemotherapy for lung adenocarcinoma, including pemetrexed, cisplatin, gemcitabine, paclitaxel, vinorelbine, docetaxel, doxorubicin, etoposide, erlotinib and gefitinib, were all analyzed in our study [19]. The chemotherapeutic response was estimated by using the R package “pRRophetic” [20]. The half maximal inhibitory concentration (IC50) of each patients of different risk group were compared.

Functional enrichment analysis

The biological functions and pathways of the key gene were elucidated through the DEGs between the high-risk and low-risk groups. GO enrichment and KEGG pathway analyses were then were assessed in DAVID database.

Correlation between the key gene and tumor immune cell infiltration

The enrichment levels of immune cells was quantified by the Tumor Purity, Estimate Score, Immune Score and Stromal Score in each sample. The tumor immune cell infiltration was calculated by Single Sample Gene Set Enrichment Analysis (ssGSEA). Then we analyzed the correlation between the key gene expression and the abundance of infiltrating immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells) via The Tumor IMmune Estimation Resource (TIMER) database [21].

Analyses of somatic mutations and TMB estimation

The somatic mutation profiles of LUAD patients were downloaded from TCGA database. The mutation frequency with number of variants/the length of exons (38 million) were calculated for each sample. The OncoPlot of the top 10 mutated genes were plotted. The detailed mutational information, including the variant classification, the number of variant type and the single-nucleotide variant (SNV) class were displayed. Then we assessed the corelation between the key gene expression and the TMB levels.

Quantitative real-time PCR and immunohistochemistry

The mRNA and protein expression of the key gene in LUAD were determined using quantitative real-time PCR (qRT-PCR) and immunohistochemistry. The clinical tissue samples of LUAD were obtained from patients who received surgery in Thoracic Oncology Department of Sun Yat-sen University Cancer Center, which was approved by the Institutional Review Committee of Sun Yat-sen University Cancer Center. The detail procedure was performed according to strict adherence to the manufacturers’ instructions.

For qRT-PCR, 24 paired LUAD and normal tissues were resected and stored in RNAlater immediately. Total RNA was extracted using TRIzol reagent. cDNA was synthesized from total RNA using cDNA reverse transcription kit (Termo Fisher Scientifc). qRT-PCR was performed using SYBR Green PCR kit (Termo Fisher Scientifc). The housekeeping gene GAPDH was used as an endogenous control. Primer information: FANCD2: 5′- AAAACGGGAGAGAGTCAGAATCA-3' (forward) and 5'- ACGCTCACAAGACAAAAGGCA-3' (reverse); GAPDH: 5′- GGAGCGAGATCCCTCCAAAAT-3' (forward) and 5'- GGCTGTTGTCATACTTCTCATGG-3' (reverse). The cycle threshold (Ct) of each gene in samples were record. Relative quantification was calculated as 2-ΔCt (ΔCt values = target gene mean Ct value - control gene mean Ct value).

Twenty LUAD immunohistochemistry samples were fixed using 10% formalin and were embedded in paraffin. Immunohistochemistry was carried out using the processed 5µm continuous sections. Samples were dewaxed with decreasing concentrations 100%, 95%, 75% and 50% of ethanol and were washed in deionized water. The sections were heated in a microwave with TE buffer pH 9.0 to retrieve antigens. Endogenous peroxidase was inhibited by incubation in goat serum. Then they were incubated in rabbit anti-FANCD2 (Proteintech, 204006-1-AP, 1:1,200) overnight at 4℃. Next, the sections were incubation with horseradish peroxidase-coupled goat anti-rabbit secondary antibody and stained using DAB Detection Kit (Polymer). The following process is cell nucleus staining, dehydration, xylene infusion, and mounting [22].

Characteristics of the LUAD patients from datasets

The flow chart of this study was shown in Fig. 1. A total of 316 LUAD patients from the TCGA cohort and 381 LUAD patients from the GEO (GSE116959) cohort were finally enrolled. The detailed clinical and tumor characteristics of the LUAD cohorts were summarized in Table 1. A total of 70 autophagy-dependent ferroptosis-related genes were identified by literatures review, which was shown in Fig. 2.

Table 1

Clinical and tumor characteristics of the LUAD cohorts
	TCGA cohort	GEO cohort
No. of patients	316	381
Age (median, range)	64 (33–86)	69 (38–89)
Gender
Female	163 (51.6%)	166 (43.6%)
Male	153 (48.4%)	215 (56.4%)
Tumor
T 1–2	275 (87.0%)	NA
T 3–4	41 (13.0%)	NA
Node
N 0–1	267 (84.5%)	NA
N 2–3	49 (15.5%)	NA
Metastasis
M 0	296 (93.7%)	NA
M 1	20 (6.3%)	NA
TNM stage
Ⅰ	164 (51.9%)	246 (64.6%)
Ⅱ	76 (24.1%)	65 (17.1%)
Ⅲ	56 (17.7%)	56 (14.7%)
Ⅳ	20 (6.3%)	14 (3.6%)
TP53
Wide type	NA	287(75.3%)
Mutant type	NA	94(24.7%)
Survival status
OS years (median)	2.19	2.24
LUAD, adenocarcinoma of lung, TCGA, The Cancer Genome Atlas, GEO, Gene Expression Omnibus, No: number, T: tumor, N: regional lymph node, M: metastasis, NA: not available, OS, overall survival.

Three steps were carried out to screen the key gene. First, 7 DEGs in Fig. 3A were selected. Second, 8 genes that had prognostic values for LUAD were selected (Fig. 3B). Third, the key gene was obtained as the intersection between DEGs and prognosis-related genes using Venn diagrams (Fig. 3C). As a result, FANCD2 was identified as the key gene, which worked as a prognosis-related differentially expressed gene in LUAD.

The mRNA and protein expression levels of FANCD2 is higher in LUAD than normal lung samples

The FANCD2 expression in different tumors was evaluated using TCGA RNA-sequencing data (Fig. 4). FANCD2 expression was significantly higher in various tumors compared with adjacent normal tissues, and the consistent findings were shown in LUAD (Fig. 5A). qPCR results provieded the biological evidence by using 24 pairs of LUAD and sample lung tissues (Fig. 6A). After examining the mRNA expression level of FANCD2 in LUAD, the protein expression level was further explored by immunohistochemistry. There is a higher expression level of FANCD2 in LUAD tissues than normal lung tissues (Fig. 6B, 6C), which is in line with the result of HPA statabase (Fig. 5B). In summary, the present results indicated that both transcriptional and translational expression levels of FANCD2 were overexpressed in patients with LUAD which may be involved in the pathogenesis of LUAD.

Prognostic Risk Model And Predictability Evaluation

The LUAD patients were stratified into high and low risk groups by the FANCD2 expression level. Table 2 showed the association of FANCD2 expression and the clinical features. A high expression of FANCD2 achieved a significant correlation with a high TNM stage (P < 0.05). In the TCGA cohort, the FANCD2 expression was defined as an independent prognostic factor after the univariate and multivariate Cox regression analyses (Fig. 7A). The patients in high risk groups have a poor survival than the low risk group in TCGA cohort. Similarly, relevant data from a GEO cohort (GSE35570) was used to validate the prognostic value of FANCD2 expression in LUAD (Fig. 7B). Besides the poor survival and a high TNM stage, the high expression of FANCD2 was also related to a high frequency of TP53 mutation (P < 0.001, Table 2). The sensitivity and specificity of the FANCD2 model was calculated by the area under ROC (TCGA cohort: AUC = 0.736, GEO cohort: AUC = 0.677), suggesting that the FANCD2 signature was effective for predicting survival of LUAD (Fig. 8). We investigated the response to chemotherapy in high- and low-risk patients with LUAD, and found that 29 chemotherapeutic drugs displayed significant differences in estimated IC50 between highand low-risk patients, and that high-risk patients with LUSC showed increased sensitivity to all 29 chemotherapies (Fig. 9).

Table 2

Baseline characteristics of the patients in different risk groups
Characteristics	TCGA-LUAD cohort			GEO-LUAD cohort
	High risk	Low risk	P value	High risk	Low risk	P value
Gender(%)			0.09			0.25
Female	71(46.4%)	88(57.1%)		112(58.9%)	103(53.9%)
Male	82(53.6%)	66(42.9%)		78 (41.1%)	88(46.1%)
Age (%)			0.17			0.99
< 65y	74(48.4%)	60(39.0%)		52 (27.4%)	52(27.2%)
≥ 65y	79(51.6%)	94(61.0%)		138(72.6%)	139(72.8)
TNM stage			0.03			0.01
Ⅰ-Ⅱ	112(73.2%)	120(77.9%)		152(80.0%)	159(83.2%)
Ⅲ-Ⅳ	41(26.8)	34(22.1%)		38(20.0%)	32(16.7%)
TP53			NA			< 0.001
Wide type	-	-		132(69.5%)	155(81.1%)
Mutant type	-	-		58(30.5%)	36(18.8%)
LUAD, adenocarcinoma of lung, TCGA, The Cancer Genome Atlas, GEO, Gene Expression Omnibus, y, years, NA: not available.

Functional Annotation Of Degs In Different Risk Groups

GO and KEGG pathway enrichment analyses were used to evaluate the possible functions and pathways of the screened DEGs. As shown in Fig. 10A, the top five GO terms were response to reactive oxygen species, regulation of peptidase activity, neutrophil degranulation, neutrophil activation involved in immune response, and neutrophil activation. The top five pathways were phagosome, antigen processing and presentation, human T-cell leukemia virus, Th17 cell differentiation, and salmonella infection by KEGG enrichment (Fig. 10B).

The results of GO analysis showed that these DEGs might be involved in response to reactive oxygen species and immune processes. The data were consistent with our results that FANCD2 is correlated with ferroptosis and immune responses. Pathway enrichment analysis revealed that DEGs may be enriched in pathways related to phagosome and antigen processing and presentation, indicating that these genes function in autophagy and immune system.

Association Between Fancd2 And Immune-related Scores

The potential immune mechanisms of LUAD were further explored through the scoring of tumor immune component (TumorPurity, ESTIMATEScore, ImmuneScore, StromalScore), and immune infiltrating cells, which all counted according to immunity-enriched groups (Fig. 11). Each patient in the LUAD cohort was scored by above indicators. The immune cell infiltration levels changed along with the FANCD2 gene copy numbers (Fig. 12). The LUAD patients with a high expression level of FANCD2 had a low ESTIMATEScore (Fig. 12A), ImmuneScore (Fig. 12B) and StromalScore (Fig. 12C), but high TumorPurity (Fig. 12D)was found in the high expression of FANCD2. Neutrophil cell infiltration levels seemed to positively associate with altered FANCD2 gene copy numbers in LUAD (cor = 0.15, P < 0.001, Fig. 13), which is consistent with the GO results of neutrophil degranulation, neutrophil activation involved in immune response, and neutrophil activation in Fig. 10A.

Landscape of mutation profiles in LUAD cohort

Mutation information of LUAD cohort was displayed in oncoplot, where various colors with annotations represented the different mutation types (Figure 14). Then, the top 5 mutated genes in LUAD with ranked percentages were exhibited, including P53 (47%), TTN (41%), MUC16 (40%), RYR2 (34%) and CSMD3 (34%). These mutations were further classified according to different mutation categories. Findings indicated that missense mutation accounted for the most fraction (Figure 15A), and single nucleotide polymorphism (SNP) occurred more frequently than insertion or deletion (Figure 15B), and C>A was the most common single nucleotide variants (SNV) in LUAD (Figure 15C). The LUAD patients with a high expression level of FANCD2 showed a higher TMB (P<0.001, Figure 15D), which suggested that FANCD2 could work as a TMB marker and play a role in prediction of response to immunotherapy.

LUAD is a common malignancy with a high morbidity and mortality [1]. The development of LUAD often involves genetic abnormalities and immune dysfunction [23]. Iron metabolism could influence malignant biological behaviors and impact the tumor microenvironment [24]. The increase of labile iron in cancer cells can both facilitate DNA replication [25], and induce the coourrence of ferroptosis [4] to participate in and accelerate tumor progression.

Ferroptosis is a programmed cell death in which multiple signaling molecules interact with each other in the tumor microenvironment and synergistically regulate tumor progression [26]. Ferroptosis has a dual role in tumour promotion and suppression [27]. On the one hand, the induced tumor cell ferroptosis inhibit tumor metastases, involve in drug resistance and influence cancer immunotherapeutic efficacy [28] [29]. On the other hand, ferroptotic damage could contribute to inflammation-related immunosuppression within the tumour microenvironment and promotes the growth of tumors [27] [30]. The role of ferroptosis in lung adenocarcinoma has not been elaborated. Our research provides a new perspective for the development of lung adenocarcinoma.

Ferroptosis was once considered as a novel cell death process which was distinct from apoptosis, necrosis, and autophagy [4]. However, studies from autophagy-deficient cells suggested that ferroptosis was a type of autophagy-dependent cell death in some conditions [8]. Autophagy, especially certain types of selective autophagy, such as ferritinophagy [31] [32], lipophagy [33], clockophagy [34] [35], and chaperone-mediated autophagy [34], could promote ferroptosis through lipid peroxidation.

FANCD2 (FA complementation group D2) contributes heterogeneously to Fanconi anemia (FA), a genetic disorder characterized by birth defects, progressive bone marrow failure, and cancer-prone phenotype [36]. The patients with aberranct expression of FANCD2 possess abnormality in chromosomal breakage and hypersensitivity to DNA crosslinking agents [37]. As an DNA damage response regulator, FANCD2 also can regulate ferroptosis sensitivity by inhibiting iron accumulation and lipid peroxidation in an autophagy-independent manner [38] [39]. FANCD2 has an intricate relationship with tumor. The heterozygous and somatic mutations of FANCD2 were reported in a variety of malignancies, including pancreatic cancers and squamous cell carcinomas [40] [41]. The overexpression of FANCD2 was involved in metastasis-prone melanomas [42] and colorectal cancer [43]. In our study, FANCD2 was identified as an autophagy-dependent ferroptosis-related key gene in the LUAD occrrence after comprehensive analysis.

In tumor microenvironment, immune cells could regulate tumour ferroptosis during cancer immunotherapy [3]. Besides, ferroptosis also could regulate immunity activity within the tumour microenvironment [30]. The potential connection between behavior of immune cells in the tumor microenvironment and ferroptosis needs to be further studied. In our study, the high expression of FANCD2 group achieved a high fraction of neutrophil, which revealed that ferroptosis-related gene FANCD2 may be closely associated with neutrophil-mediated tumor immunity.

In accordance with above findings, the contents of the antigen processing and presentation were enriched by KEGG analyses. In adaptive immunity, neutrophils play a significant role in internalizing antigen and regulating antigen-specific responses [44]. When ferroptosis occurred, the immunogenic signals, such as lipid mediators, were released by the death cells. Subsequently, the antigen-presenting cells including neutrophils were attracted to the site of ferroptotical cells [30]. A multitude of recuited neutrophils further activated the immune system to resist the invasion of pathogenic factors. Abnormal and uncotrolled ferroptosis may be implicated with invalid inmmunity [30]. Our research indicated that high expression of FANCD2 induced the aberrent ferroptosis and further contributed to the abnormality of anti-tumor immunity in patients with LUAD.

TMB level has demonstrated utility in selecting patients for response to immunetherapy and has proven to be an important biomarker for patient selection. Patients who are in high TMB benefit more from immunotherapy, which provided a new avenue to make LUAD treatment more precise [45]. In our study, the LUAD patients with a high expression level of FANCD2 achieved a high TMB, indicating that these patients may gain more benefit from immunotherapy than those with a low FANCD2 expression level. Among the mutation genes, tumor suppressor gene inactivation, such as P53, is very common in LUAD [46]. P53 activation has been explored to be essential in some other activities to suppress tumor progression [47] [48], whereas the anti-P53 activity traditionally drives cell senescence, cell cycle arrest and apoptosis [49]. Additionally, P53 was reported to be correlated with ferroptosis, and it could inhibit cystine uptake and sensitize cells to ferroptosis. Study revealed that the sensitivity of ROS-induced ferroptosis was markedly increased in P53-activated cells [50]. Our study founds that P53 is the most frequently mutated gene and positively correlated with a higher FANCD2 expression level, which indicated that the P53 mutation may activate the FANCD2-mediated ferroptosis and increase the response of immunotherapy in LUAD.

Our study identified a novel autophagy-dependent ferroptosis-related gene, FANCD2 gene which was proved to be independently associated with OS in LUAD and may serve as prognostic factor for LUAD. FANCD2 expression level was negatively correlated with immune infiltrating levels but positively correlated with somatic mutation in LUAD, which indicated that FANCD2 may act as a potential inhibitor to interfere with immune cells and revealed the potential relationship and interaction between ferroptosis and immunity in LUAD pathogenesis. More mechanistic studies are needed to verify the role and function of FANCD2-mediated feroptosis in the LUAD in future.

LUAD, lung adenocarcinoma; TMB, tumor mutation burden; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; DEGs, Differentially expressed genes; OS, overall survival; K-M, Kaplan-Meier; ROC, receiver operating characteristic; GO, Gene ontology; KEGG, Kyoto encyclopedia of genes and genomes; TIMER, Tumor Immune Estimation Resource; HGNC, HUGO Gene Nomenclature Committee; FDR, false discovery rate; GEPIA, Gene Expression Profiling Interactive Analysis; HPA, The Human Protein Atlas database; IC50, The half maximal inhibitory concentration; ssGSEA, Single Sample Gene Set Enrichment Analysis; SNV, single-nucleotide variant; qRT-PCR, real‑time quantitative RT‑PCR; Ct, cycle threshold; mRNA, messenger RNA; FANCD2, FA Complementation Group D2; SNP, single nucleotide polymorphism; INS, insertion; DEL, deletion.

Ethics and consent to participate

The clinical tissue samples of LUAD were obtained from patients who received surgery in Thoracic Oncology Department of Sun Yat-sen University Cancer Center. The informed consent obtained from study participants was written in Informed Consent Form. The study was approved by the Institute Research Medical Ethics Committee of Sun Yat-sen University Cancer Center. The reference number is YB2018–85. All the experiment protocol for involving human data was in accordance with the guidelines of Declaration of Helsinki.

Consent for publication

All authors agree to the publication of our work entitled “Comprehensive analysis of the autophagy-dependent ferroptosis-related gene FANCD2 in lung adenocarcinoma” to BMC cancer.

Availability of data and material
All data generated or analysed during this study are included in this published article.

Competing interests

No authors report any conflict of interest.

Funding

This work was supported by the “National Natural Science Foundation of China (81871986)”. The funder had no role in the design of this study and will not have any role during its execution, analyses, interpretation of the data, or decision to submit results.

Authors' contributions

HK and QN conceived and designed the study. DN, HM and MY performed the analysis, prepared the figures and tables and wrote the main manuscript. YF and ZS were involved in critically revising the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank all patients who provide their tissue sample in this study.

Siegel, R.L., K.D. Miller, and A. Jemal, Cancer statistics, 2020. CA Cancer J Clin, 2020. 70(1): p. 7–30.
Siegel, R.L., K.D. Miller, and A. Jemal, Cancer statistics, 2019. CA Cancer J Clin, 2019. 69(1): p. 7–34.
Wang, W., et al., CD8(+) T cells regulate tumour ferroptosis during cancer immunotherapy. Nature, 2019. 569(7755): p. 270–274.
Dixon, S.J., et al., Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell, 2012. 149(5): p. 1060–72.
Stockwell, B.R., et al., Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell, 2017. 171(2): p. 273–285.
Tang, D., et al., The molecular machinery of regulated cell death. Cell Res, 2019. 29(5): p. 347–364.
Xie, Y., et al., Ferroptosis: process and function. Cell Death Differ, 2016. 23(3): p. 369–79.
Zhou, B., et al., Ferroptosis is a type of autophagy-dependent cell death. Semin Cancer Biol, 2020. 66: p. 89–100.
Wang, Y., et al., Iron Metabolism in Cancer. Int J Mol Sci, 2018. 20(1).
Ganz, T. and E. Nemeth, Iron homeostasis in host defence and inflammation. Nat Rev Immunol, 2015. 15(8): p. 500–10.
Zhang, F., et al., Engineering Magnetosomes for Ferroptosis/Immunomodulation Synergism in Cancer. ACS Nano, 2019. 13(5): p. 5662–5673.
Alexandrov, L.B., et al., Signatures of mutational processes in human cancer. Nature, 2013. 500(7463): p. 415–21.
Van Allen, E.M., et al., Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat Med, 2014. 20(6): p. 682–8.
Chalmers, Z.R., et al., Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med, 2017. 9(1): p. 34.
Schumacher, T.N., C. Kesmir, and M.M. van Buuren, Biomarkers in cancer immunotherapy. Cancer Cell, 2015. 27(1): p. 12–4.
Rhodes, D.R., et al., Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia, 2007. 9(2): p. 166–80.
Tang, Z., et al., GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017. 45(W1): p. W98-W102.
Colwill, K., G. Renewable Protein Binder Working, and S. Graslund, A roadmap to generate renewable protein binders to the human proteome. Nat Methods, 2011. 8(7): p. 551–8.
Duma, N., R. Santana-Davila, and J.R. Molina, Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment. Mayo Clin Proc, 2019. 94(8): p. 1623–1640.
Geeleher, P., N. Cox, and R.S. Huang, pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014. 9(9): p. e107468.
Li, T., et al., TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res, 2017. 77(21): p. e108-e110.
Lin, F. and Z. Chen, Standardization of diagnostic immunohistochemistry: literature review and geisinger experience. Arch Pathol Lab Med, 2014. 138(12): p. 1564–77.
Remark, R., et al., The non-small cell lung cancer immune contexture. A major determinant of tumor characteristics and patient outcome. Am J Respir Crit Care Med, 2015. 191(4): p. 377–90.
Pfeifhofer-Obermair, C., et al., Iron in the Tumor Microenvironment-Connecting the Dots. Front Oncol, 2018. 8: p. 549.
Thanan, R., et al., Inflammation-induced protein carbonylation contributes to poor prognosis for cholangiocarcinoma. Free Radic Biol Med, 2012. 52(8): p. 1465–72.
Jiang, M., et al., Targeting ferroptosis for cancer therapy: exploring novel strategies from its mechanisms and role in cancers. Transl Lung Cancer Res, 2020. 9(4): p. 1569–1584.
Chen, X., et al., Broadening horizons: the role of ferroptosis in cancer. Nat Rev Clin Oncol, 2021. 18(5): p. 280–296.
Viswanathan, V.S., et al., Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature, 2017. 547(7664): p. 453–457.
Zheng, D.W., et al., Switching Apoptosis to Ferroptosis: Metal-Organic Network for High-Efficiency Anticancer Therapy. Nano Lett, 2017. 17(1): p. 284–291.
Friedmann Angeli, J.P., D.V. Krysko, and M. Conrad, Ferroptosis at the crossroads of cancer-acquired drug resistance and immune evasion. Nat Rev Cancer, 2019. 19(7): p. 405–414.
Gao, M., et al., Ferroptosis is an autophagic cell death process. Cell Res, 2016. 26(9): p. 1021–32.
Hou, W., et al., Autophagy promotes ferroptosis by degradation of ferritin. Autophagy, 2016. 12(8): p. 1425–8.
Bai, Y., et al., Lipid storage and lipophagy regulates ferroptosis. Biochem Biophys Res Commun, 2019. 508(4): p. 997–1003.
Yang, M.H., et al., Clockophagy is a novel selective autophagy process favoring ferroptosis. Science Advances, 2019. 5(7).
Liu, J., et al., Autophagic degradation of the circadian clock regulator promotes ferroptosis. Autophagy, 2019. 15(11): p. 2033–2035.
Longerich, S., et al., Stress and DNA repair biology of the Fanconi anemia pathway. Blood, 2014. 124(18): p. 2812–9.
Timmers, C., et al., Positional cloning of a novel Fanconi anemia gene, FANCD2. Mol Cell, 2001. 7(2): p. 241–8.
Song, X., et al., FANCD2 protects against bone marrow injury from ferroptosis. Biochem Biophys Res Commun, 2016. 480(3): p. 443–449.
Sertorio, M., et al., Fancd2 Deficiency Impairs Autophagy Via Deregulating The Ampk/Foxo3a/Akt Pathway. Blood, 2013. 122(21).
van der Heijden, M.S., et al., Fanconi anemia gene mutations in young-onset pancreatic cancer. Cancer Res, 2003. 63(10): p. 2585–8.
Wreesmann, V.B., et al., Downregulation of Fanconi anemia genes in sporadic head and neck squamous cell carcinoma. ORL J Otorhinolaryngol Relat Spec, 2007. 69(4): p. 218–25.
Kauffmann, A., et al., High expression of DNA repair pathways is associated with metastasis in melanoma patients. Oncogene, 2008. 27(5): p. 565–73.
Ozawa, H., et al., FANCD2 mRNA overexpression is a bona fide indicator of lymph node metastasis in human colorectal cancer. Ann Surg Oncol, 2010. 17(9): p. 2341–8.
Vono, M., et al., Neutrophils acquire the capacity for antigen presentation to memory CD4(+) T cells in vitro and ex vivo. Blood, 2017. 129(14): p. 1991–2001.
Chan, T.A., et al., Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Annals of Oncology, 2019. 30(1): p. 44–56.
Berkers, C.R., et al., Metabolic Regulation by p53 Family Members. Cell Metabolism, 2013. 18(5): p. 617–633.
Valente, L.J., et al., p53 efficiently suppresses tumor development in the complete absence of its cell-cycle inhibitory and proapoptotic effectors p21, Puma, and Noxa. Cell Rep, 2013. 3(5): p. 1339–45.
Brady, C.A., et al., Distinct p53 transcriptional programs dictate acute DNA-damage responses and tumor suppression. Cell, 2011. 145(4): p. 571–83.
Brooks, C.L. and W. Gu, Ubiquitination, phosphorylation and acetylation: the molecular basis for p53 regulation. Current Opinion in Cell Biology, 2003. 15(2): p. 164–171.
Jiang, L., et al., Ferroptosis as a p53-mediated activity during tumour suppression. Nature, 2015. 520(7545): p. 57–62.

No competing interests reported.

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Comprehensive Analysis of the Autophagy-dependent Ferroptosis-related Gene FANCD2 in Lung Adenocarcinoma

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Characteristics of the LUAD patients from datasets

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