2023 Volume 70 Issue 2 Pages 185-196
Iron overload can lead to chronic complications, serious organ dysfunction or death in the body. Under hypoxic conditions, the body needs more iron to produce red blood cells to adapt to the hypoxic environment. The prevalence of iron overload in the Tibetan population is higher than that in the Han population. To explore the molecular mechanism of iron-overload in the Tibetan population, this study investigated the transcriptome of the Tibetan iron overload population to obtain differentially expressed genes (DEGs) between the iron-overloaded population and the normal iron population. Functional enrichment analysis identified key related pathways, gene modules and coexpression networks under iron-overload conditions, and the 4 genes screened out have the potential to become target genes for studying the development of iron overload. A total of 28 pathways were screened to be closely related to the occurrence and development of iron overload, showing that iron overload is extremely related to erythrocyte homeostasis, cell cycle, oxidative phosphorylation, immunity, and transcriptional repression.
IRON is a metal element critical to the normal physiological processes of many organisms, ferritin is the major intracellular iron storage protein, and in many cases, serum ferritin reflects iron storage [1]. The pathophysiological mechanism of iron overload is not fully understood, and it is known that many inherited diseases of iron overload can lead to abnormal regulation of iron absorption and storage due to genetic defects, such as hemochromatosis, cataract, and microcytic hypochromic anemia. Some previous studies have found that iron overload occurs mainly in middle-aged men [2], iron overload increases the risk of diabetes [3], and the prevalence of iron overload in adult men on the Tibetan Plateau is relatively high [4]. Normal mice in a hypoxic environment for 3 days showed 2- to 3-fold increased iron absorption [5]. We speculate that due to the influence of hypoxia and low pressure, the Tibetan population requires the production of more red blood cells to cope with the hypoxic environment, while hemoglobin levels are essential for iron production. During the adaptation process, hemoglobin rises, the body needs to take in more iron, and the absorption and transport of iron are uncontrolled, resulting in iron overload. Hepcidin is a key regulator of iron homeostasis that controls iron absorption and macrophage release, but hepcidin gene polymorphisms in high-altitude populations are not associated with iron overload susceptibility [6], so we speculate that low or high expression of certain genes causes iron overload in the body. Excess iron is stored in tissues such as the liver, heart, and bone marrow and can lead to progressive organ damage. In our previous study, it was found that methylation modification is involved in the regulation of iron homeostasis in Tibetan populations [7]. This study assumes that iron overload leads to changes in the expression of related genes and intends to investigate the expression of RNA through the transcriptome. These genes play a certain role in different biological processes. Through functional enrichment of differentially expressed genes (DEGs), we screened for the occurrence and development of iron overload. The related pathways and genes will help to study the molecular mechanism of iron overload and provide insight into finding potential important targets of iron overload.
Patients who met the screening conditions and lived in plateau areas (altitude >2,000 meters) who came to our hospital for physical examination and treatment from June to October 2020 were selected, and 16 subjects were screened according to ferritin content and underlying disease conditions. Informed consent was obtained from all individual participants included in the study. All study procedures were performed in accordance with the Declaration of Helsinki. Individuals with a serum ferritin (SF) content less than 200 μg/L were placed in the normal iron group, and those with an SF content greater than 1,000 μg/L were in the iron overload group. The exclusion criteria were as follows: patients with hematological diseases such as anemia, polycythemia vera, and myelodysplastic syndrome; patients with secondary hypertension; patients with definite atherosclerotic cardiovascular disease (ASCVD), such as coronary heart disease, coronary stent postoperatively, after coronary artery bypass grafting, stroke, peripheral vascular disease and revascularization, carotid endarterectomy or carotid artery stenting; patients with heart failure with reduced systolic function (EF <50%); creatinine >3 times the normal value, glomerulonephritis, or renal insufficiency; ALT >3 times the normal value; patients with hepatic insufficiency; patients with chronic obstructive pulmonary disease, or sleep apnea syndrome; congenital heart disease and heart surgery; and history of venous thromboembolism. The basic clinical information of the samples is shown in Table 1. Four milliliters of blood was collected from each person and refrigerated in an ultralow temperature refrigerator at –80°C for subsequent experiments.
| sample | serum ferritin (ug/L) | age | sex | group |
|---|---|---|---|---|
| S1 | 2,113 | 48 | male | TH |
| S2 | 1,985 | 48 | male | TH |
| S3 | 2,641 | 60 | male | TH |
| S4 | 1,519 | 37 | male | TH |
| S5 | 1,079 | 30 | male | TH |
| S6 | 1,270 | 43 | male | TH |
| S7 | 1,088 | 58 | male | TH |
| S8 | 1,660 | 47 | male | TH |
| S9 | 120.3 | 37 | male | TL |
| S10 | 175.6 | 20 | male | TL |
| S11 | 138.7 | 24 | male | TL |
| S12 | 99.21 | 23 | male | TL |
| S13 | 144.1 | 22 | male | TL |
| S14 | 191.7 | 49 | male | TL |
| S15 | 168.2 | 20 | male | TL |
| S16 | 154.2 | 60 | male | TL |
Note: “S” stands for “sample number”; “TH” stands for “Tibetan high ferritin group”; “TL” stands for “Tibetan normal ferritin group”.
Total RNA from purified whole blood in PAXgene tubes was extracted using the PAXgene Blood RNA Kit (Qiagen). The samples were analyzed by agarose gel electrophoresis for RNA integrity and DNA contamination. RNA purity (OD260/280 and OD260/230 ratios) was detected using a NanoPhotometer spectrophotometer. RNA integrity was detected using an Agilent 2100 bioanalyzer. RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First-Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized using random hexamer primers and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3' ends of DNA fragments, NEBNext adaptors with hairpin loop structures were ligated to prepare for hybridization. To preferentially select cDNA fragments 250~300 bp in length, the library fragments were purified with an AMPure XP system (Beckman Coulter, Beverly, USA). Then, 3 μL of USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. Finally, PCR products were purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform, and 150 bp paired-end reads were generated.
Raw data processingRaw data (raw reads) in fastq format were first processed by fastp 0.19.7. Reads containing adapters, reads containing poly-N (where N indicates that the base information cannot be determined) and low-quality reads (the number of bases with Qphred ≤20 accounting for more than 50% of the entire read length from raw data) were removed. At the same time, the Q20, Q30 and GC contents were calculated for the clean data. All subsequent analyses were performed based on clean data. The reference genome file (GRCh38.p12) was downloaded from the genome website (https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_000001405.38/), and paired-end clean reads were aligned with the reference genome using HISAT2v2.0.5 with default settings. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in the National Genomics Data Center (Nucleic Acids Res 2021) [8], China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA001880), and are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human.
DESeq2 software was used to normalize the raw readcount using the DeSeq method performed by the estimateSizeFactors functions. The normalized count value for each gene (as a measure of the transcript or gene level) was then calculated based on the length of the gene, and reads mapped to that gene were calculated. Differential expression analysis between the TH group and TL group was performed using DESeq2 software. According to the threshold |log2foldchange| > 0, p < 0.05 (the method of Benjamini and Hochberg) as the screening criteria, DEGs between the two groups were obtained. The expression of some genes was verified by real-time quantitative PCR.
DEG function enrichmentPathway and process enrichment analysis was performed on DEGs using the Metascape website [9] (https://metascape.org/gp/index.html) using GO Biological Processes, GO Cellular Components, GO Molecular Functions, KEGG Pathway, Reactome Gene Sets, Canonical Pathways, WikiPathways and PANTHER Pathway enrichment, and the Min Overlap was collected as 3, the p value was <0.01, and min enrichment was >1.5, and datapoints were grouped into clusters based on their membership similarity. A subset of enriched terms was selected and rendered as a network plot, where terms with a similarity >0.3 were connected by edges. We selected the terms with the best p values from each of the 20 clusters, with the constraint that there were no more than 15 terms per cluster and no more than 250 terms in total. The network was visualized using Cytoscape. Pathways enriched for the 620 hub-DEGs most associated with SF levels from Weighted gene coexpression network analysis (WGCNA) were classified using the cluego [10] plugin in Cytoscape software.
WGCNAWGCNA was used to construct gene coexpression networks and identify core modules and core genes. We used the gene expression profile, the median absolute deviation (MAD) of each gene was calculated separately, and the top 50% of the genes with the smallest MAD values were eliminated. The “goodSamplesGenes” method of the R software package WGCNA was used to remove the outlier genes and samples, and further WGCNA was used to construct a scale-free coexpression network. A Pearson correlation matrix and average linkage method were performed for all paired genes, and then the power function A_mn = |C_mn|^β was used to construct a weighted adjacency matrix, set according to the result. The soft threshold was 12. Average linkage hierarchical clustering was performed according to TOM-based dissimilarity measures to classify genes with similar expression profiles into gene modules. The minimum size (genome) of the gene dendrogram was 30, the sensitivity value was 3, and the threshold for module merging was 0.25.
GSEAWe performed gene set enrichment analysis (GSEA) to further understand the function of DEGs and obtained GSEA software from GSEA 3.0 software [11] from the GSEA (http://software.broadinstitute.org/gsea/index.jsp) website.The samples were divided according to ferritin levels and divided into two groups, and the c2.cp.v7.4.symbols.gmt sub from the Molecular Signatures Database [12] (http://www.gsea-msigdb.org/gsea/downloads.jsp) was downloaded. Curated gene sets were collected to evaluate related pathways and molecular mechanisms, and grouped based on gene expression profiles (Supplementary Table S1) and phenotypes, with the minimum gene set to 5, the maximum gene set to 5,000, and resampling one thousand times. A p value of <0.05 was considered statistically significant.
A total of 1,830 DEGs were screened (the DEG list is in Supplementary Table S1): 961 DEGs were up-regulated, and 869 DEGs were downregulated. The results of functionally enrichment (Fig. 1a and 1b) showed that these DEGs were significantly enriched in cytokine signaling in immune system (R-HSA-1280215), cellular responses to stimuli (R-HSA-8953897), cellular responses to stimuli (R-HSA-8953897), cellular responses to stimuli cycle (R-HSA-1640170), cellular chemical homeostasis (GO:0055082), Huntington disease (hsa05016), processes cellular chemical homeostasis (GO:0055082) and other terms, indicating that iron overload mainly affected the body’s cellular response and other physiological processes.
a: Bar graph of the top 20 enriched term clusters across DEGs, colored according to p values. b: Top 20 enriched Gene Ontology clusters across DEGs. Colored by cluster ID, each term is represented by a circle node, where its size is proportional to the number of input genes falling under that term, and its color represents its cluster identity (i.e., nodes of the same color belong to the same cluster). Terms with a similarity score >0.3 are linked by an edge (the thickness of the edge represents the similarity score).
The soft threshold was set to 12 (Fig. 2a), the dissimilarity of eigengenes of modules was calculated, modules with a distance less than 0.25 were merged, and, finally 5 coexpression modules were obtained (Fig. 2b). To further study the correlation between the clinical feature ferritin level and gene expression, the gene Trait Significance (GS) of the correlation between the gene and SF level was calculated, and the Module Membership (MM) of the module feature vector and gene expression was calculated at the same time. According to the screening criteria (|MM| > 0.8 and |GS| > 0.1), the overall brown and green module genes were most associated with SF levels (Fig. 2c), 620 genes with high connectivity in the clinically significant modules (serum ferritin level) were identified as hub-DEGs, and Supplementary Table S2 details the hub-DEG list.
a: Schematic diagram of soft threshold selection. b: Hierarchical clustering dendrogram of dissimilarity based on module eigengenes. Each color in the figure indicates that each gene on the corresponding cluster tree belongs to the same module. The vertical distance represents the distance between two nodes (between genes), and the horizontal distance is meaningless. The line “Dynamic Tree Cut” shows the modular classification of genes. The line “merge dynamic” shows the modular classification of genes with a threshold of 0.25 for modular merging. c: Heatmap of the ferritin level correlation between the eigengenes of each WGCNA module. The rightmost column of the figure is 5 modules that are marked with different colors. The SF column represents the correlation between modules and the clinical trait of ferritin level. The upper right triangle of each row represents the correlation, with red representing a positive correlation and green representing a negative correlation. The upper right triangle of each row represents the significance of the correlation, purple indicates significant and white indicates nonsignificant.
Hub-DEG function clustering: The 620 hub-DEGs related to ferritin levels were analyzed by the cluego plugin in Cytoscape software, and they were mainly divided into 13 functional groups (Supplementary Table S3 shows all the pathway information enriched by hub-DEGs). Items related to viral transcription accounted for 25.55% of the total, items related to the type I interferon signaling pathway accounted for 22.47%, items related to the heterocycle metabolic process accounted for 17.18%, items related to hemopoiesis accounted for 2.64%, items related to the cellular protein metabolic process accounted for 3.52%, and items related to oxidative phosphorylation accounted for 6.17%. This result shows that even after the occurrence of iron overload in Tibetans, the biological functions, such as hematopoiesis, cellular protein metabolism, and oxidative phosphorylation, undergo some changes, and iron overload has a tendency to develop into other diseases. The results from several studies have shown that iron homeostasis and mitochondrial dysfunction frequently co-occur with inflammation in vivo [13].
Pathways associated with iron metabolism dysregulation and iron overload disease phenotypes: In the CTD [14] website (https://ctdbase.org/), we found for information on pathways related to iron overload and dysregulation of iron metabolism, among which 27 terms (Table 2) were the same as those enriched in hub-DEGs (Fig. 3 and Supplementary Table S3), among which include aerobic respiration, cellular nitrogen compound metabolic process, erythrocyte homeostasis, gene expression, oxidative phosphorylation, regulation of cellular metabolic process, mitochondrial ATP synthesis coupled electron transport, etc. Illustrate the changes of multiple biological processes in the body during iron overload in this study, and again verify that iron overload causes irreversible damage to cell homeostasis during cellular biochemical processes [15].
| Phenotype Term Name | Phenotype Term ID | Disease Name |
|---|---|---|
| aerobic respiration | GO:0009060 | Iron Overload |
| aerobic respiration | GO:0009060 | Iron Metabolism Disorders |
| cellular nitrogen compound metabolic process | GO:0034641 | Iron Overload |
| cellular nitrogen compound metabolic process | GO:0034641 | Iron Metabolism Disorders |
| cellular protein modification process | GO:0006464 | Iron Overload |
| cellular respiration | GO:0045333 | Iron Overload |
| cellular respiration | GO:0045333 | Iron Metabolism Disorders |
| cellular response to stress | GO:0033554 | Iron Overload |
| erythrocyte homeostasis | GO:0034101 | Iron Overload |
| erythrocyte homeostasis | GO:0034101 | Iron Metabolism Disorders |
| G2/M transition of mitotic cell cycle | GO:0000086 | Iron Overload |
| gene expression | GO:0010467 | Iron Metabolism Disorders |
| innate immune response | GO:0045087 | Iron Metabolism Disorders |
| intracellular signal transduction | GO:0035556 | Iron Metabolism Disorders |
| mitochondrial ATP synthesis coupled electron transport | GO:0042775 | Iron Metabolism Disorders |
| mitochondrion organization | GO:0007005 | Iron Metabolism Disorders |
| mitotic cell cycle | GO:0000278 | Iron Overload |
| mitotic cell cycle process | GO:1903047 | Iron Overload |
| mitotic cell cycle process | GO:1903047 | Iron Metabolism Disorders |
| myeloid cell homeostasis | GO:0002262 | Iron Overload |
| oxidative phosphorylation | GO:0006119 | Iron Overload |
| oxidative phosphorylation | GO:0006119 | Iron Metabolism Disorders |
| protein modification process | GO:0036211 | Iron Overload |
| protein transport | GO:0015031 | Iron Metabolism Disorders |
| protein ubiquitination | GO:0016567 | Iron Overload |
| regulation of cellular metabolic process | GO:0031323 | Iron Overload |
| regulation of gene expression | GO:0010468 | Iron Metabolism Disorders |
| regulation of intracellular signal transduction | GO:1902531 | Iron Overload |
| response to organic substance | GO:0010033 | Iron Metabolism Disorders |
| RNA catabolic process | GO:0006401 | Iron Overload |
| translation | GO:0006412 | Iron Overload |
| transport | GO:0006810 | Iron Overload |
| viral genome replication | GO:0019079 | Iron Overload |
Functional enrichment analysis of the 620 hub-DEGs was performed using the ClueGO plug-in for Cytoscape. Each node represents a pathway entry, the connection of the nodes represents the correlation of the entry, and the same color represents belonging to the same functional group and the enrichment of genes on the entry.
GSEA was also performed to validate signature changes in mRNA. GSEA identified 1 pathway (p value = 0.0352) (Fig. 4), the sumoylation of transcription cofactors pathway, that was downregulated in the iron overload group, reflecting the relative repression of transcriptional regulation, and identifying pathways preferentially induced or inhibited by combination therapy. PARK7, PIAS2, SIN3A, and HIPK2 were obtained from the intersection of CTD- and ClueGo- related genes and GSEA pathway related genes. PARK7 was upregulated in the iron-overloaded population, and PIAS2, SIN3A, and HIPK2 were downregulated in the iron-overloaded population. The results of the present study suggest that sumoylation of transcription cofactors is one of the biological pathways closely related to iron overload.
GSEA network graph with nodes representing the enriched reactome pathways (at 25% FDR)
Three randomly selected genes (EPO/IL15/FLVCR2) and four hub-DEGs, PARK7, PIAS2, SIN3A, and HIPK2, were verified by qPCR experiments. The results of qPCR verification were consistent with the trend of transcriptome sequencing (Fig. 5).
qPCR results of PIAS2, PARK7, SIN3A, HIPK2, EPO, IL15 and FLVCR2 genes (****, p < 0.0001; ***, p < 0.001; *, p < 0.05).
When the iron ion content in the blood exceeds the binding capacity of transferrin, free iron in the blood is produced, and this free iron will be deposited in the tissue, leading to iron overload. Environmental changes can induce cellular responses and change the physiological functions of cells [16]. Iron is an important component of hemoglobin and red blood cell production. The erythropoietin gene EPO is expressed at a higher level in the iron-overloaded Tibetan population than in the normal population, and the related DEG is upregulated. In our previous studies [7], most of the genes involved in iron absorption and transport were closely related to the biosynthesis of red blood cells, indicating that iron overload may be related to compensatory polycythemia in Tibetans. The significantly enriched GO and KEGG pathways were mainly related to hematopoiesis, cellular protein metabolism, oxidative phosphorylation, etc. At the same time, these DEGs are also related to a variety of diseases, suggesting that iron overload will lead to develop to a variety of diseases.
Iron overload leads to organ damage through the harmful effects of reactive oxygen species [17]. Pathways related to iron overload and iron metabolism disorders (Table 2) involve cells, erythrocytes, mitochondria, gene expression, mitosis, oxidative phosphorylation and other processes. Some studies have reported that gene expression [18, 19] and gene expression regulation [20, 21] are associated with dysregulation of iron metabolism, consistent with the findings of this study. Posttranslational modifications, such as ubiquitination and protein degradation, precisely regulate the cellular levels and functions of proteins involved in iron metabolism [22]. Erythrocyte homeostasis is highly correlated with body iron levels, and higher plasma transferrin receptor levels and sTfR levels suggest that adolescents with gastritis will develop recessive iron deficiency [23]. Human heme oxygenase-1 (HO-1)-targeted mice exhibit growth retardation, anemia, iron deposition, and susceptibility to stress injury [24]. In this study, JMJD6, AHSP, E2F2, CD151, FOXM1, SOD1, and SLC4A1 genes related to erythrocyte homeostasis (GO: 0034101) were highly expressed in iron overloaded people, and genes such as SLC7A1, ERFE, MYB, EPAS1, and PRNP were expressed at low levels in the Tibetan iron-overloaded population suggesting that erythrocyte homeostasis is affected during iron overload, possibly because the body requires a greater amount of iron under hypoxic conditions to generate erythrocytes to adapt to the hypoxic environment, so the body is exposed to iron under hypoxic conditions. Iron levels will rise, so the expression levels of genes related to red blood cell homeostasis will also change. Regulation of iron-related genes by different cytokines may allow time-dependent control of changes in iron metabolism during inflammation and may be associated with chronic inflammation, infection, and the cancer environment [25], which was in accordance with our results. High-fructose feeding consumption induces iron overload in Sprague-Dawley rats, which inhibits the hepatic antioxidant defense system and increases lipid peroxidation [26]. Except for ATP7A, all DEGs in the oxidative phosphorylation (GO: 0006119) pathway were highly expressed, suggesting that the body’s energy supply is affected by iron overload. These studies have shown that iron overload is closely related to erythrocyte homeostasis and oxidative phosphorylation.
The four important genes we screened out are all strongly associated with the development of diseases during iron overload, such as Parkinson’s disease, migraine, and pulmonary hypertension. The results of our previous study [7] showed that the PIAS2, SIN3A, and HIPK2 genes all have differentially methylated regions, indicating that methylation also participates in the occurrence and development of iron overload by interfering with the transcription of the PIAS2, SIN3A, and HIPK2 genes. Transcription of PARK7 in the human population may not be affected by methylation. The 10 key differentially methylated genes (ACO1, CYBRD1, FLVCR1, HFE, HMOX2, IREB2, NEDD8, SLC11A2, SLC40A1 and TFRC) involved in iron absorption and transport [7] were all involved in transcription in this study, and IRBE2 and NEDD8 were significantly different in iron overloaded population. PARK7 plays an important role in cell protection from oxidative stress and cell death as an oxidative stress sensor and redox-sensitive chaperone and protease, and loss of PARK7 activity in a zebrafish model of Parkinson’s disease affects iron-responsive elements (IREs) in the brain transcriptome. The expression of the genome has differential effects, and there is a dysregulation of iron homeostasis. HIPK2 is involved in the regulation of ferritin H and other antioxidant detoxification genes in genotoxic stress conditions [27]. HIPK2 was also associated with the cellular nitrogen compound metabolic process (GO: 0034641), cellular response to stress (GO: 0033554), gene expression (GO: 0010467), intracellular signal transduction (GO: 0035556), regulation of cellular metabolic process (GO: 0031323), regulation of gene expression (GO: 0010468), and response to organic substance (GO: 0010033). The HIPK2 gene was in a low expression state during iron overload in this study. PIAS2, a protein inhibitor that activates STAT2, plays a key role in the pathogenesis of autoimmune and inflammatory diseases [28], and PIAS2 expression was significantly lower in total migraine patients than in controls [29]. In this study, PIAS2 was also observed to be reduced in iron-overloaded populations. Inhibition of SIN3A activity promotes the differentiation of pluripotent cells into functional neurons [30], protective and beneficial effects of SIN3A in pulmonary hypertension [31], and SIN3A was underexpressed during iron overload in this study.
GSEA identified 1 pathway (p value <0.05), the sumoylation of the transcription cofactor pathway was significantly upregulated in the iron overload group. Reflecting relative transcriptional regulation inhibition, identifying pathways preferentially induced or inhibited by combination therapy.
In this study, through WGCNA, GSEA, functional enrichment and other analytical methods and online searches on the CTD website, 28 pathways related to iron overload diseases were screened showing that iron overload is related to metabolism, cell cycle, oxidative phosphorylation, immunity and inflammation. Additionally, iron overload promotes cellular immunity, cell cycle arrest and apoptosis and inhibits transcriptional regulation, suggesting that the body will develop multiple diseases under iron overloaded conditions. The 4 genes screened out have the potential to become target genes for studying the development of iron overload. However, this study has certain limitations. The mean age of the iron-overloaded population in this study was older than the mean age of the iron-normal population, consistent with previous studies showing that ferritin levels are age-related [32]. Although we considered the correlation between the level of ferritin and it when extracting core DEGs, the effect of age on ferritin and transcriptome cannot be excluded in this study. In conclusion, this study establishes a certain molecular basis for the prevention and treatment of iron overload and related diseases. Further research to investigate the molecular mechanism and pathological mechanism of iron overload is crucial for the treatment of iron overloaded people in Tibet.
Iron overload is closely related to erythrocyte homeostasis, cell cycle, oxidative phosphorylation, immunity, and transcriptional repression. The screened PARK7, HIPK2, PIAS2 and SIN3A genes have the potential to become target genes for studying the development of iron overload. This study establishes a certain molecular basis for the prevention and treatment of iron overload and related diseases.
Funding: This study was supported by Projects of Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region (Hospital.C.T.) (2021-YJ-8); The central government guides the local science and technology development fund project, the science and technology department of Tibet (No. XZ202201YD0013C); Sichuan Science and Technology Program (Grant No. 2019YJ0642).
Preservation: The samples are stored in Biobank of Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region.
Ethics approval: All procedures performed in studies involving human participants were by the ethical standards of the Ethics Committee of Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region in Chengdu (Approval ID: 2018, 64). The trial was registered with the Chinese Clinical Trials Registry (ChiCTR1900025123). Informed consent: Informed consent was obtained from all individual participants included in the study. The authors declare that they have no conflict of interest.
