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.