Data mining and function analysis of a key ferroptosis target FANCD2 positively correlated with PD-L1 expression

doi:10.21203/rs.3.rs-5319387/v1

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Data mining and function analysis of a key ferroptosis target FANCD2 positively correlated with PD-L1 expression

https://doi.org/10.21203/rs.3.rs-5319387/v1

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Iron is required for the control of ferroptosis, a pattern of programmed cell death brought on by the buildup of reactive oxygen species and lipid peroxidation. Inducing ferroptosis may have great therapeutic promise for tumor cells, according to an increasing number of studies. However, it is unclear how ferroptosis works to treat lung cancer. For prognostic analysis of expression of 25 ferroptosis-related genes, consensus clustering analysis, LASSO model development, and association analysis with PD-L1, we gathered clinical and molecular data of LUAD patients from the TCGA database and the GEO database. We discovered that the ferroptosis gene FANCD2 had the best prognostic value of any gene based on the findings. Then, using multicolor immunofluorescence to identify the mutual regulatory link between FANCD2 and PD-L1 in LUAD tissues, we discovered that both of these genes were highly expressed and co-expressed in LUAD tissues. Finally, using four study axes—mutation, tumor microenvironment, immune infiltration, and pan-cancer—we discovered that FANCD2 may have a direct or indirect role in the aforementioned processes, influencing how well LUAD patients would fare.

In conclusion, this research offers proof in favor of finding novel, potential immune targets for treatment as well as refining PD-L1 antibody immunotherapy for LUAD. In-depth analysis of the FANCD2 gene and the creation of associated medications may enhance prognosis and increase median survival time in LUAD patients.

FANCD2

immune infiltration

tumor microenvironment

LUAD

PD-L1

Cancer will be the world's most common cause of death in the twenty-first century and the biggest barrier to global efforts to extend life expectancy. Lung cancer is both the most often diagnosed and the deadliest of these tumors ^[1]. Non-small cell lung cancer (NSCLC) makes up the majority of instances of lung cancer, accounting for around 85% of all occurrences of lung cancer ^[2]. Of them, lung adenocarcinoma (LUAD) makes for 40% of all instances of lung cancer. Additionally, LUAD has a bad prognosis since it metastasizes to other organs such as the liver, adrenal glands, bones, and brain^[3–4]. LUAD may go unnoticed since it might remain localized for a long time before symptoms arise ^[5].

Precision oncology, which incorporates tumor genotyping into treatment decisions, has assisted in improving patient outcomes and quality of life in comparison to traditional chemotherapy since the development of targeted drug therapy^[6–7]. The use of chemotherapy is increasingly being replaced by medications like dacomitinib, gefitinib, ceritinib, crizotinib, and many others^[8–11].PD1/PDL1 immune checkpoint inhibitors have been discovered and utilized in the treatment of lung adenocarcinoma) ^[12]. A regulating immunosuppressive receptor for T cell activity, PD-1 is an immunosuppressive receptor that is expressed on activated T cells.T lymphocytes, epithelial cells, endothelial cells, and tumors have been shown to express PD-L1, which is known as a PD1 ligand. PDL1 inhibits T cell activation and cytokine production when it interacts with its receptor, and in the tumor microenvironment, this connection allows tumor cells to evade the immune system by inactivating cytotoxic T cells. Although lung cancer is being treated with monoclonal antibodies that target PD1 or PDL1, such as pembrolizumab, nivolumab, and atezolizumab^[12], the outlook for LUAD patients is still dismal^[13].

Although inhibition of apoptosis or necroptosis is insufficient to inhibit ferroptosis, suggesting that ferroptosis is a distinct form of necrosis from autophagy, apoptosis, and other forms of necrosis, ferroptosis is an iron-dependent programmed cell death process that typically coexists with other cell death programs^[14][15]. Due to the high iron content of cancer cells and their increased sensitivity to ferroptosis induction, ferroptosis has been thought to be a promising cancer therapy, and as a result, inducing ferroptosis may also have significant therapeutic potential for tumor cells with ferroptosis-sensitive mutations^[16][17].To enhance T cell depletion in the tumor microenvironment, tumor cells upregulate PD-L1 expression through PD-1/PD-L1 interactions, and they mutate to avoid being recognized by T cells that are unique to the tumor^[18]. Liproxstatin-1 (a ferroptosis inhibitor), which inhibits ferroptosis, decreases the anticancer activity of tumor-killing T cells and anti-PD-L1 antibodies^[19][14]. To our knowledge, however, there isn't a thorough analysis of the association between the expression of ferroptosis-related genes in LUAD and immune cells and immunological checkpoints.

In this study, we thoroughly examined the relationship between the expression of ferroptosis-related genes in LUAD and patient prognosis, immune cell infiltration, and the level of immune cell infiltration. We then successfully identified a key ferroptosis gene, FANCD2, that affects the prognosis of LUAD based on three essential conditions: upregulation of ferroptosis gene expression in LUAD, strong correlation with prognosis, and positive correlation with PD-L1. Improving the diagnosis, management, and prognosis of LUAD, will increase our understanding of the ferroptosis gene's regulatory and functional aspects and provide the groundwork for the identification of novel immunotherapeutic targets.

2.1 Ethical statement

The Clinical Trials Ethics Committee of the Affiliated Hospital of Southwest Medical University, China, gave its approval to this investigation (ethical review number: KY2019276). Before collecting samples, volunteers completed an informed consent form that had been approved by the ethics committee. All procedures followed the pertinent rules and regulations. This research conformed with the Helsinki Declaration.

2.2 Data sources

For RNA sequencing data (level 3) of LUAD tumor and para carcinoma samples, raw counts and the accompanying clinical information were received from The Cancer Genome Atlas (TCGA) dataset (https://portal.gdc.com). The GEO dataset used is from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the download data format is MINiML. 25 genes related to ferroptosis were chosen from previous literature^[20].

2.3 KM survival analysis

The log-rank test and univariate Cox regression were used to calculate p-values and hazard ratios (HR) with 95 percent confidence intervals (CI) for Kaplan-Meier curves. The R program version 4.0.3 was used to carry out analysis techniques and R packages (R Foundation for Statistical Computing, 2020). Statistics were judged significant at p <0.05.

2.4 Consensus clustering

With a maximum of 6 clusters and 100 trials to extract 80% of the entire sample, the consistency analysis was carried out using the R package Consensus Cluster Plus (v1.54.0), clusterAlg = "hc", innerLinkage = "ward.D2". The R software tool heatmap was used to evaluate the clustering heat map (v1.0.12). The genes with a variance of more than 0.1 were retained in the gene expression heat map, and the top 25% of the input target genes were extracted for display after being sorted from highest to lowest according to the variance value if the number of input target genes exceeded 1000.

2.5 LASSO prognostic model

Data download: Samples with RNA-seq data and clinical information were retained, and samples were then used for follow-up analysis. The STAR-counts data and clinical information of LUAD tumors were downloaded from the TCGA dataset repository (https://portal.gdc.com), from which data in TPM format were extracted. The KM survival analysis was tested using log-rank to compare the survival rates of the aforementioned two or more groups, and timeROC analysis was used to determine the predictive model's precision. The aforementioned study was carried out using the R software's glmnet package, and the least absolute shrinkage and selection operator (LASSO) regression approach was employed for feature selection with 10-fold cross-validation.

2.6 Genetic correlation

The R program ggstatsplot implemented the two-gene correlation plots, whereas the R package heatmap displayed the multi-gene correlation plots. Without a normal distribution, correlations between quantitative variables were described using Spearman's correlation analysis, and p-values under 0.05 were regarded as statistically significant.

2.7 Multicolor immunofluorescence

The samples were frozen tissues from LUAD that had been analyzed and recorded at the Southwest Medical University Affiliated Hospital's Department of Pathology. The immunofluorescence method was developed based on this literature ^[21], where the primary antibodies were FANCD2 (Abcam, USA) and PDL1 (Abcam, USA), and the secondary antibodies were anti-mouse AlexaFluor®488 (CST, USA) and anti-rabbit AlexaFluor®594 (CST, USA).

2.8 Genetic mutation landscape

The Cancer Genome Atlas (TCGA) dataset (https://portal.gdc.com), which contains information about LUAD tumors, was used to gather RNAseq data (level 3), mutation maf data, and associated clinical data. The map tools package in R software was used to download and display somatic mutations in LUAD patients.

2.9 Enrichment pathway

mRNA differential expression was investigated using the R program Limma (version:3.40.2). In TCGA, adjusted P-values were examined to account for false-positive findings. The threshold mRNA differential expression screen was established as "Adjusted P < 0.05 and log2 (fold change) > 1 or log2 (fold change) -1". Functional enrichment: We analyzed the GO function of possible mRNAs and enriched the KEGG pathway using the ClusterProfiler tool in R.

3.1 LUAD and typical paracancerous tissues were compared for survival rates and differences in the expression of genes linked to ferroptosis.

We created a heat map of the expression of 25 ferroptosis-related genes in LUAD patients using data from the TCGA database. We discovered that 13 of these genes (RPL8, HSPB1, GPX4, GLS2, DPP4, SLC7A11, CISD1, ATP5MC3, HSPA5, SLC1A5, CARS1, FANCD2and CS) showed a significant upregulation compared to nearby normal tissues, while 10 genes (ATL1, NCOA4, NFE2L2, LPCAT3, TFRC, ACSL4, FDFT1, ALOX15, CDKN1A , and SAT1) showed a trend of low expression compared to adjacent normal tissues(Figure. 1. A). We discovered that the majority of the ferroptosis-related genes had positive correlations with one another, whereas a small subset of genes exhibited negative correlations with one another (Figure.1.B). Then, 25 genes were subjected to a Kaplan-Meier (KM) survival analysis to determine the impact of each gene on survival. The findings revealed that six genes (ATP5MC3, GLS2, DPP4, FANCD2, GLS2, and SLC7A11) among them were significantly associated with the prognosis of LUAD patients (p < 0.05) (Figure. 1.C-H).

3.2 Consensus clustering of genes associated with ferroptosis

In order to conduct a consensus clustering analysis, we downloaded LUAD data from the TCGA database.With a maximum of 6 clusters and 100 trials to extract 80% of the entire sample, the consistency analysis was carried out using the R package ConsensusClusterPlus (v1.54.0), clusterAlg = "hc", innerLinkage = "ward.D2". Tumor sample subsets were divided into several categories. Additionally, we determined the outcomes of cluster consensus and item consensus, and the resulting data revealed 2–6 subgroups (Figure2.A). All patients may be correctly divided into two categories when k=2 (Figure2.B). We also presented the ConsensusClusterPlus consistency clustering results heat map at k=2. Different colors represent different categories, as well as heat maps of ferroptosis-related gene expression in different subgroups, with red representing high expression and blue representing low expression(Figure2.C). The KM survival curves of the 2 subgroup samples in the TCGA dataset were then examined (p < 0.05). (Figure2.D).

3.3 Construction of LASSO model of ferroptosis gene in LUAD

We included gender, age, pT, pN, and pM stage as important clinical variables in a univariate Cox regression analysis of 25 genes related to ferroptosis (Figure. 3. A), and 9 genes were chosen (p < 0.05)(CISD1, FANCD2, CDKN1A, ATP5MC3, SLC7A11, GLS2, ALOX15, DPP4 , and ACSL4). Following feature selection using the least absolute shrinkage and selection operator (LASSO) regression algorithm and 10-fold cross-validation, timeROC analysis was used to compare the prediction accuracy and risk scores of the nine candidate genes, and exactly all nine candidate genes were potential predictors (Figure.3.B-C). Then, we calculated their associated risk coefficients and divided the patients into high-risk and low-risk groups according to the median expression. Our analysis of their relationship with survival using the KM survival method revealed that the low-risk group had a better prognosis than the high-risk group. The area under the curve (AUC) for one year was 67 percent, for three years it was 66.5 percent, and for five years it was 60.4%. We also examined the prognostic effectiveness of risk variables using ROC curves. The protective genes DPP4, ALOX15 , and GLS2 exhibited elevated expression in low-risk patients, while CISD1, SLC77A11, FANCD2, ATP5MC3, ACSL4 , and CDKN1A were significantly expressed in high-risk patients, according to the correlation heat map we further constructed for these nine possible predictors (Figure. 3.D).

3.4 Identification of FANCD2, a crucial ferroptosis gene

We used the R package pheatmap to show the connection of 25 ferroptosis-related genes with PD-L1 in order to further understand the regulatory role of ferroptosis-related genes in LUAD and to identify critical ferroptosis genes. In which the following genes are positively correlated with PD-L1: ACSL4, CARS1, CDKN1A, CS, DPP4, FANCD2, HSPB1, LPCAT3, MT1G, NCOA4, NFE2L2, SAT1, TFRC, ATL1, and the following genes are negatively linked with PD-L1: ATP5MC3, FDFT1, GLS2, RPL8, SLC1A5, SLC7A11 (Figure4.A). Then, using the upregulated expression in LUAD (a), strong correlation with prognosis (b), high expression in the high-risk patient group of the LASSO model (c), and positive correlation with PD-L1 expression (d) as necessary conditions, we drew Venn diagrams to identify key predictors of ferroptosis-related genes in LUAD. Ultimately, we discovered only one key predictor, FANCD2 (Figure4. B). Using the GSE32863 and GSE75037 datasets from the GEO database, we checked the expression of FANCD2 in LUAD, and the findings revealed that FANCD2 was significantly expressed in LUAD (Figure4.C, D).

3.5 Expression of FANCD2 and PD-L1 in LUAD

We used a multicolor immunofluorescence assay to find the expression of FANCD2 and PD-L1 in the cancer tissues of LUAD patients in order to further confirm the expression of FANCD2 in LUAD and the link with PD-L1. In LUAD tissues, FANCD2 and PD-L1 were both substantially expressed (Figure5.A, B, C, D). And we discovered that the cell membrane co-expressed FANCD2 and PD-L1 (Figure5.E, F).

3.6 Landscape of FANCD2 gene mutations

Using R's maftools package, we downloaded and visualized somatic mutations in LUAD patients. We discovered that the FANCD2 somatic mutation rate in LUAD patients was only 0.98 percent (Figure. 6A), less than 1 percent, and the possibility of mutation is very low. The somatic landscape of the LUAD tumor cohort was then displayed using Oncoplot on the high and low FANCD2 expression groups separately. In the high FANCD2 expression group, the 10 genes TP53, TTN, MUC16, CSMD3, RYR2, LRP1B, USH2A, ZFHX4, and FLG had a higher mutation rate than in the low FANCD2 expression group (Figure6.B).

3.7 Expression of FANCD2 and associated pathways

We used the Limma package of R software to research the differential expression of mRNAs and discovered 144 up-regulated genes and 44 down-regulated genes in the FANCD2 high expression group(Figure7.A). We conducted a functional enrichment analysis of the data to further establish their putative roles and the correlation of associated pathways. The enrichment results of KEGG pathways revealed 20 pathways that were positively correlated with FANCD2 expression, and the enrichment results of pathways annotated with differentially upregulated genes GO revealed 21 pathways that were positively correlated with FANCD2 expression (Figure. 7B). These findings imply that FANCD2 may affect a number of signaling pathways to control the occurrence and progression of LUAD and affect the prognosis of patients. We calculated the correlation between gene expression and pathway scores in order to obtain the correlation between FANCD2 and related pathways. The results revealed that FANCD2 expression was positively correlated with cellular response to hypoxia, tumor proliferation characteristics, the PI3K/AKT signaling pathway, MYC target genes, DNA replication, the G2M checkpoint, and DNA damage restoration, and negatively correlated with ECM-related genes, angiogenesis, inflammatory response, P53 signaling pathway, TGFB, collagen formation, ECM degradation(Figure7.C).

3.8 Association of FANCD2 expression levels with immune infiltration

The tumor microenvironment and tumor development, progression, and medication resistance are all significantly influenced by tumor-infiltrating immune cells^[22]. Cancer-related fibroblasts are the most prevalent and one of the most significant cells linked with malignancy and are essential for cancer progression among the stromal cells that make up the tumor microenvironment^[23]. Therefore, we investigated the association between the degree of tumor-associated fibroblast infiltration and FANCD2 gene expression in LUAD tumors using the TIDE, XCELL, MCPMETHER, and EPIC algorithms. We found that FANCD2 expression was negatively connected with tumor-associated fibroblast infiltration levels according to the great majority of algorithms (Figure8.A).

we also investigated the relationship between FANCD2 and other immune infiltrating cells in LUAD tumors， using various algorithms including TIDE, XCELL, MCPMETHER, CIBERSORT, QUANTISEQ, and EPIC to analyze FANCD2 expression about infiltration levels of CD4+ T cells, CD8+ T cells, B cells, DC cells , and macrophages. In CD4+ T cell analysis, we discovered a positive correlation between FANCD2 expression and the infiltration values of T cell CD4+, T cell CD4+ memory, T cell CD4+ memory activated, T cell CD4+ Th1 and T cell CD4+ Th2, while a negative correlation was found between the infiltration values of T cell CD4+ effector memory and T cell CD4+ memory resting (Figure8.B).In the B cell investigation, we discovered that FANCD2 expression was favorably connected with B cell naïve. Infiltration values and negatively correlated with B cell memory and B cell plasma infiltration values (Figure8.C). We discovered that, in the majority of the methods used for the CD8+ T cell analysis, the expression of FANCD2 was positively linked with the infiltration values of T cell CD8+, T cell CD8+ naïve and T cell CD8+ effector memory(Figure8.D).In the examination of macrophages, we discovered that FANCD2 expression was positively correlated with infiltration values of Macrophage, Macrophage M0, Macrophage M1, and Macrophage/Monocyte, and negatively correlated with infiltration values of Macrophage M2(Figure8.E). In DC cell analysis, there was a positive association with Plasmacytoid dendritic cell infiltration value and a negative correlation with the infiltration value of Myeloid dendritic cell, Myeloid dendritic cell activated, and Myeloid dendritic cell resting (Figure8.F).

3.9 Single cell sequencing and immunohistochemistry

We analyzed the expression of FANCD2 in different cell clusters by means of single-cell sequencing as a way to show the distribution of the gene (Figure A-D).We also characterized the difference in FANCD2 expression between lung adenocarcinoma and normal tissues (Figure E).The results showed that the gene was highly expressed in lung adenocarcinoma tissues and low or absent in normal tissues.This also suggests that FANCD2 is involved in the process of lung adenocarcinoma development and regulates the proliferation of tumor cells. This also makes this gene a potential immune prognostic target.

With an active FA pathway, FANCD2 is a crucial protein in the Fanconi anemia (FA) DNA repair process. FANCD2 expression levels are strongly correlated with tumor grade^[24], and upregulation of FANCD2 is positively connected with tumor size and a poor prognosis in breast cancer^[25], ovarian cancer^[26][27], glioblastoma^[24], and endometrial cancer^[28]. It is impossible to isolate the intricate interactions with the tumor microenvironment (TME) from the formation and progression of cancer^[29][30]. TME is composed of various immune cells,mesenchymal-origin cells, and the extracellular matrix^[31][33], of which immune infiltrating cells are important determinants of tumor growth and progression, response to therapy, and patient prognosis^[33][34][35]. High FANCD2 expression in LUAD is associated with a poor prognosis for the patient, but it is unclear from published studies how the expression of this gene is associated with the level of immune cell infiltration in the tumor microenvironment, and whether it has a competitive inhibitory or synergistic effect on promoting LUAD tumor cell proliferation and tumor progression. As a result, we revealed the molecular properties of the FANCD2 gene at several levels, including gene expression and gene mutation, using information from various databases, including TCGA.To understand the FANCD2 gene's function in the growth and possible regulatory mechanisms of LUAD tumors as well as its connection to immune infiltration, we carried out extensive bioinformatics research on FANCD2 in LUAD tumors.

In order to determine if certain ferroptosis genes have an influence on LUAD prognosis, we first examined the expression of ferroptosis-related genes in LUAD and their prognostic impact using the TCGA database. We next conducted a consensus clustering analysis and constructed the LASSO model. Many malignancies are presently being successfully treated with antibodies that disrupt the interaction of PD-1 with its ligand PD-L1^[36], but the underlying processes are still unclear^[37]. Considering this aspect, to further uncover the most valuable ferroptosis genes, in tandem with the previous study, we examined the relationship between ferroptosis-related genes and PDL1 and discovered the particular ferroptosis gene FANCD2. We then used multicolor immunofluorescence in vitro to confirm the strong relationship between FANCD2 and PDL1 in LUAD. As a result, we hypothesized that FANCD2 could be engaged in the interaction between PD-1 and PD-L1 to affect the prognosis of LUAD patients and that a more thorough investigation of FANCD2 would be predicted to increase the efficacy of PD-L1 antibody treatment for lung adenocarcinoma.TMB is the number of nonsynonymous mutations in somatic cells inside a certain genomic region, which can infer the capability and degree of neoantigen creation by tumors and the success of immunotherapy for a range of tumor types^[38]. Several clinical studies have confirmed significant clinical benefit rates in oncology patients with high-TMB treated with immune checkpoint inhibitors, including anti-CTLA-4 therapy for melanoma^[39], anti-PD-L1 therapy for uroepithelial carcinoma^[40], and anti-PD-1 therapy for non-small cell lung and colorectal cancers ^[41][42]. We discovered a strong link between FANCD2 and TMB in LUAD, and after comparing the mutation landscapes of the mutation rate of FANCD2 and the high and low FANCD2 expression groups, we discovered that the high expression group had more missense mutations than the low expression group did. Therefore, we hypothesize that LUAD patients may experience a better prognosis following therapy with immune checkpoint inhibitors because of the high expression of FANCD2 and the elevated TMB.

The microenvironment around the tumor affects the incidence, development, metastasis, and invasion of tumor cells, which has significant effects on the development, prognosis, and treatment of tumors^{[22][43][44][45]}. Cancer-associated fibroblasts are among the most significant cells associated with malignancy, are the most prevalent stromal cells in the tumor microenvironment, are currently regarded as one of the most active cell types in the tumor microenvironment, and are crucially involved in the progression of cancer^{[23][46][47][48]}. Using several algorithms in LUAD, we discovered that FANCD2 expression was favorably connected with the fibroblast infiltration value under the EPIC algorithm, but FANCD2 expression was negatively correlated with the fibroblast infiltration value under XCELL and TIDE. Although the findings under various methods varied, these results showed a substantial correlation between FANCD2 and fibroblast infiltration in the tumor microenvironment. In-depth research must be done to determine the precise mechanism of action between the two in increasing LUAD tumor cell proliferation and development.

Research on CD4+ T cells is ongoing in both fundamental and clinical immunology^[49], and an increasing body of evidence suggests that CD4+ T cells recognize antitumor responses and thereby play a significant role in the tumor microenvironment^[50][51]. By identifying MHC class II molecules linked with tumors, CD4+ T cells have a great anticancer impact. However, the majority of solid tumors lack MHC class II molecules, which restricts CD4+ T cells' functionality at the tumor site^[52]. Furthermore, it is unknown how specific CD4+ status affects immune responses in the tumor microenvironment and how these responses are influenced by systemic therapies, such as immunotherapy^[53].In our research, we discovered a strong correlation between TH1- and TH2-type CD4+ T cell infiltration and FANCD2 expression in LUAD. When CD4+ T cells were stimulated to multiply and differentiate into TH0, they were encouraged by various interleukins and then differentiated into TH1 and TH2 types, with TH1-type differentiation being primarily induced by cytokines IL-12 and IFN-y, which are primarily involved in cellular immunity, and TH2-type differentiation being primarily induced by IL-4, which is involved in humoral immunity^[54].TH1-type cells cause the induction of cytotoxic T lymphocytes with enhanced effector functions, which include tumoricidal activity, the ability to target tumor migration and penetrate the tumor extracellular matrix, downregulation of suppressor molecules to overcome the blockage of the suppressed immune response, and formation of memory cytotoxic T lymphocytes to provide long-term tumor surveillance^[55][56]. The role of TH2-type cells in the immune role in antitumors is unknown. In LUAD, high expression of FANCD2 leads to an increase in TH1-type and TH2-type cells, whether this affects the balance of helper CD4+ T cells in the microenvironment, thereby suppressing immune function, attenuating immune surveillance, and increasing immune escape from tumors, will be further investigated. We also discovered that the infiltration of CD8+ T cells, B cells, DC cells, and macrophages was affected by the aberrant expression of FANCD2 in LUAD. All of the aforementioned findings point to the strong relationship between FANCD2 and the tumor microenvironment. The aberrant expression of FANCD2 may also cause disruption of the homeostasis of the tumor microenvironment, which is a key factor in how FANCD2 influences the prognosis of LUAD.

We discovered that the expression of FANCD2 was positively linked with pathways involved in DNA replication-related pathways, the PI3K/Akt/mTOR signaling pathway, the cellular response to hypoxia, and MYC target genes. Hypoxia is one of the key physiological and microenvironmental differences between tumors and normal tissues. In tumors, hypoxia is prevalent, it is associated with increased genetic instability, progression, and metastasis, and it also inhibits tumor response to cytotoxic and targeted therapies^[57][58]. The PI3K/Akt/mTOR signaling pathway can inhibit glycogen synthase kinase 3β activity by phosphorylating its N-terminal serine thereby increasing the accumulation of cyclin D1, promoting cell cycle and improving cell proliferation^[59], and in tumors, the PI3K/Akt/mTOR signaling pathway is frequently altered to promote growth, proliferation, and progression^[60][61].MYC belongs to the family of proto-oncogenic transcription factors^[62], which are nuclear DNA-binding transcription factors responsible for coordinating cell proliferation, metabolism, and survival, and are the most common amplifying oncogenes in human cancers genes^[62][63]. During DNA replication, FANCD2 is involved in various processes of DNA replication^[64][65][66]. FANCD2 and all of the aforementioned pathways showed a positive correlation in our study, and in LUAD patients, FANCD2 may obstruct the aforementioned pathways and thereby affect the prognostic quality of patients. However, the precise mechanism of action needs to be confirmed through specific experimental studies.

Overall, we screened the FANCD2 gene from ferroptosis-associated genes to be highly valuable for the prognosis of LUAD patients. For FANCD2, we dissected the impact of this gene on LUAD in terms of mutation landscape, TMB, associated pathways, immune infiltration, and pan-cancer analysis, and found that the FANCD2 gene was co-expressed with the PD-L1 gene in lung adenocarcinoma tissues through in vitro studies. Based on these results, the ferroptosis gene FANCD2 may represent a potential target for the immune anti-tumor therapy of lung adenocarcinoma, and thorough investigation and the development of associated antibody drugs would enhance the prognosis of lung adenocarcinoma treatment and lengthen patient survival.

DATA AVAILABILITY STATEMENT

Publicly accessible datasets were used to collect the data for this investigation. These data are available here:https://portal.gdc.com, https://www.ncbi.nlm.nih.gov/geo/.

AUTHOR CONTRIBUTIONS

HC, XH, XX and JL proposed the study idea and designed the study. HC, XH and SN performed data analysis, and data validation, and drafted the manuscript. HC, LL and YY performed the experimental validation. QY, ZH revised the manuscript. XX and JL contributed to the supervision of the study. All authors read and approved the final version.

FUNDING

This work was supported by the project of Science and Technology Department of Sichuan Province ( 2023NSFSC0727); the Sichuan Science and Technology program (2022YFS0608-B1); The project Luzhou Science and Technology Bureau （2023LZXNYDJ020；2023SYF100); the Project of National College Students Innovation and Entrepreneurship Training Program (S202210632317).

CONFLICT OF INTEREST

The authors declare that the submitted work was not carried out in the presence of any personal, professional, or financial relationships that could potentially be constructed as a conflict of interest.

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Data mining and function analysis of a key ferroptosis target FANCD2 positively correlated with PD-L1 expression

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