A novel cuproptosis-related prognostic gene profiles in preeclampsia

doi:10.21203/rs.3.rs-2475428/v1

Download PDF

Research Article

A novel cuproptosis-related prognostic gene profiles in preeclampsia

https://doi.org/10.21203/rs.3.rs-2475428/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 10 Jan, 2024

Read the published version in BMC Pregnancy and Childbirth →

You are reading this latest preprint version

Background

Preeclampsia (PE) is one of the main causes of poor maternal and fetal outcomes, whose pathogenesis is complex and remains unclear. Cuproptosis is a novel identified form of programmed cell death. However, the link between cuproptosis and clinical outcome in PE is still less known.

Methods

Using RNA sequencing data in GEO database, we conducted functional enrichment analysis of GO, KEGG and GSEA, supported by linear regression model and ROC curve analysis, and summarized the role of cuproptosis related genes in preeclampsia.

Results

2831 differentially expressed genes (DEGs) related to PE were primarily summarized through multiple database analysis. Further intersected with 19 reported cuproptosis related genes (CRGs), and 5 CRGs were considered to be related to the pathogenesis of PE, including "NFE2L2", "PDHA1", "PDHB", "DLD", "GLS". Functional enrichment analysis of DEGs, CRGs and hub genes revealed identified "NFE2L2" as a key central gene. Pearson correlation analysis was used to analyze and showed that CRGs could be related to several maternal and fetal outcome factors, including the highest pregnancy blood pressure, placenta weight, umbilical blood flow PI, and neonatal weight. Linear regression equation revealed that the expression of "NFE2L2" was negatively correlated with the highest pregnancy blood pressure and umbilical blood flow PI but positively correlated with placental weight and neonatal weight. Further utilize of qPCR showed that the expression of these CRGs were significantly lower in placental tissues.

Conclusions

The cuproptosis pattern evaluated in our study may be used as potential prognostic factors in patients with PE and could provide new insights into disease progression.

Cuproptosis

Preeclampsia

Cell death

Preeclampsia (PE) is one of the unique progressive diseases of multiple systems in pregnancy. It usually manifests as new hypertension and proteinuria after 20 weeks of pregnancy, or new hypertension and terminal organ dysfunction with or without proteinuria, with an incidence rate of 5-10%[1]ually develops progressively with gestational weeks, and the only way to cure it is to terminate the pregnancy. Therefore, exploring predictive indicators related to the occurrence of PE is of utmost importance to better predict and treat PE and improve maternal and fetal outcomes.

Copper is one of the metal elements necessary for various biological processes[2][3]. The fluctuation of copper levels in cells can regulate the occurrence and development of diseases[4]-[5]. Several drugs related to regulating copper ions have been used in clinical anti-cancer treatment, including copper ion carriers (disulfiram, dithiocarbamate, elesclomol, etc.) and copper chelators (tributyl ether, tetrathiomolybdate, etc.)[6][7]. In recent years, cuproptosis, as a novel molecular mechanism regulating cell death, has been proven to participate in the occurrence and development of many diseases. Unlike the known controlled cell death modes of apoptosis, scorch death, necrotic apoptosis, and iron death, cuproptosis occurs through the direct combination of copper and the fatty acylation component of the tricarboxylic acid cycle (TCA)[8][9]. This leads to the aggregation of fatty acylated proteins, the loss of iron-sulfur cluster proteins, and protein toxicity stress and cell death. Previous domestic and foreign studies have identified a number of genes related to cuproptosis, which may provide a new strategy for predicting the prognosis of patients with PE.

In this study, we explored the differentially expressed genes (DEGs) in placental tissues of normal patients and PE patients, compared them with cuproptosis related genes (CRGs), and verified the common gene profiles in multiple databases and clinical patterns, so as to clarify the importance of cuproptosis in the development of PE and provide potential predictive indicators for the occurrence of PE.

2.1 Multi-omics data source and preprocessing

RNA sequencing dataset (GSE75010) and corresponding clinical data of patients with PE and non-PE were downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo). The data set contained gene expression data of 77 normal placental tissues and 80 PE placental tissues. The genes related to cuproptosis were derived from cuproptosis articles. The R language "limma" software package was used to analyze the gene expression difference between PE patients and normal tissues. In order to obtain more DEGs, | LogFC > 0.1 | and p < 0.05 were set as thresholds. The visualization of DEG is realized by building volcano maps, heat maps, etc. Later, the CRGs were used to intersect with DEGs and obtain the DEGs related to cuproptosis between PE patients and normal pregnant women. Also, the differences in expression between normal pregnant women and PE pregnant women were analyzed.

2.2 Gene network and gene enrichment analysis

Microarray technology and bioinformatics analysis make it possible to identify gene changes at the genomic level. In recent years, in addition to various other analyses, bioinformatics methods have been widely used to analyze microarray data to identify DEGs. The central genes were identified by protein-protein interaction (PPI) network analysis and ten algorithms of the cytoHubba plug-in. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to determine the potential functions of biomarkers.

2.3 Weighted gene co-expression network analysis (WGCNA)

The R language "WGCNA" software package was used to construct a gene co-expression network. First, after clustering the samples according to clinical information, an outlier threshold of 60 was set to screen out outliers. Next, based on the soft threshold parameter β, power function f (x) = x β was used to Convert the Pearson correlation matrix to a weighted adjacency matrix. The appropriate soft threshold was determined. After selecting the power of 6, the weighted adjacency matrix was transformed into a topological overlap matrix (TOM) with topological overlap (TO) based dissimilarity (1-TOM), and clustering was performed through TOM. Finally, genes with similar expression patterns were classified into modules according to the difference in TOM of each gene for average linkage hierarchical clustering. To further analyze each module, the minimum size (genome) of the gene tree was 30. If the distance was < 0.25, the modules were merged. After obtaining co-expressed gene modules, central genes were screened according to the criteria of gene weight (GS) > 0.2, GS p value < 0.05, and module weight (MM) > 0.8 MM p value < 0.05.

2.4 Collection of placental tissue and real-time fluorescence quantitative PCR

Quantitative real-time PCR was used to measure the total amount of products after each PCR cycle with fluorescent chemicals in the DNA amplification reaction. This is a method for quantitative analysis of specific DNA sequences in the sample to be tested by internal or external reference methods.

2.5 Statistical analysis

We first made a descriptive statistical analysis of the PE patients in the database. Shapiro Wilk test was used to determine whether continuous data had a normal distribution. If the data conformed to the normal distribution, a T-test was used to compare the general situation of normal and PE patients and the maternal and fetal outcomes, which is expressed by mean ± standard deviation. If the data did not conform to the normal distribution, the Kruskal Wallis rank sum test was used to compare the differences between the two groups, which is expressed by the median value [Q1 quartile - Q3 quartile]. The categorical variables are expressed by frequency and proportion (n [%]). The Chi-square test was used to compare the differences between groups. We used Benjamin − Hochberg method to make multiple corrections. FDR < 0.05 indicated a statistically significant standard. Through database analysis, we used linear regression to correlate CRGs with maternal and fetal outcomes of PE. The predictability of biomarkers was analyzed by the receiver operating characteristic (ROC) curve. The p value is bilateral, and p < 0.05 indicates statistical significance.

3.1 Differential gene expression and cuproptosis related genes in placenta of PE

According to PE pregnant women and normal pregnant women, a three-line table was made to show the differences in age, BMI, peak blood pressure (systolic/diastolic blood pressure), termination week, umbilical blood flow S/D ratio, Apgar score (1 min, 5 min), etc. between the two groups (Table 1). The results showed that the pregnancy termination in PE patients occurred more than one week earlier compared to normal pregnant women (p = 0.013), and the BMI and peak blood pressure (systolic and diastolic blood pressure) of pregnant women were significantly higher than that of normal pregnant women (p = 0.0264; p = 0.000; p = 0.000), while the 1-minute Apgar score of newborns of PE pregnant women was significantly lower than that of normal pregnant women (p = 0.0432). There was no significant difference in other factors between the two groups (p > 0.05).

After data standardization and differential gene expression analysis, 2831 DEGs were found in normal placental tissue and PE placental tissue, including 1722 up-regulated genes and 1109 down-regulated genes (Figure 1A-1B), compared with PE placental tissue, and partial DEG expression was listed by corrected p-value through a heat map (Figure 1C).

Table 1 Comparison of general conditions of pregnant women between the two groups

	Normal(N=77)	PE(n=80)	P-value	statistic
Maternal age(Year)	33.2(5.35)	33.2(5.89)	0.995	-0.0063
Maternal BMI(kg/m²)	24.5(4.84)	26.5(5.64)	0.0264^*	-2.2448
Gestational week(Week)	34.0(4.73)	32.3(3.57)	0.013^*	2.5149
Umbilical cord diameter	1.21(0.290)	1.16(0.393)	0.38	0.8809
Mean.umbilical.PI	1.30(0.463)]	1.39(0.379)	0.319	-1.0046
Maximum.systolic.bp	136(23.5)	170(18.1)	<0.001^*	-10.0190
Maximum.diastolic.bp	85.3(14.7)	107(9.73)	<0.001^*	-11.0239
Apgar.score..1.min	7.93(1.82)	7.30(1.76)	0.0432^*	2.0409
Apgar.score..5.min	8.70(1.06)	8.71(0.725)	0.935	-0.0821

*p < 0.05

3.2 Multiple analysis of GO, KEGG, and GSEA

In order to understand the biological processes involved in these DEGs, they were enriched and analyzed through GO, KEGG, and GSEA analysis. The GO pathway revealed that DEGs are mainly enriched in oxidative stress, hypoxia reaction, intercellular connection, GTPase (Figure 2A-2F). KEGG pathway found that DEGs were mainly enriched in the PI3K pathway and atherosclerosis (Figure 3A-3D). After genome analysis by the GSEA pathway, it was found that DEGs were mainly concentrated in purine metabolism, cGMP PKG signal pathway, GABA synapse, and other pathways(Figure 4A-4B).

3.3 WGCNA analysis of cuproptosis genes associated with PE

First, we statistically analyzed the differences in the expression of 19 cuproptosis genes in normal placental tissues and PE placental tissues in the database, finding that "NFE2L2", "PDHA1", "PDHB", "DLD", and "GLS" had significant differences in the expression between the two groups (Figure 5A), and the five differential genes had low expression in PE (Table 2&Figure 5B). According to genes with a similar expression, DEGs were grouped into modules using WGCNA, and 28 modules were finally identified (Figure 6A). Then, Pearson correlation coefficient analysis was conducted to connect each module with the clinical characteristics of pregnant women (including systolic blood pressure, diastolic blood pressure, umbilical artery blood flow ratio, etc.) and maternal and fetal outcomes (neonatal APGAR score, neonatal weight, placental weight, etc.). The results showed that the negative correlation coefficient of the blue module was high, indicating that it has a protective role in the occurrence and development of PE (Figure 6B). According to the cut-off standard (| MM | > 0.8 and GS > 0.2), 62 genes in this module were identified as central genes with high weight. Finally, the Wayne map was constructed to intersect DEGs, CRGs, and hub genes. The intersection of DEGs, hub genes, and CRGs from GSE75010 obtained a common central gene: NFE2L2 (Figure 6C-6D).

Table 2. Intersection of differential gene and copper death gene

Gene	logFC	AveExpr	T	P Value	adj.P.Val	β	change
NFE2L2	0.16	10.70	5.49	1.58E-07	5.43E-06^*	6.98	Down
PDHA1	0.13	9.34	4.49	1 38E-05	2 29E-04^*	2.72	Down
PDHB	0.11	8.92	3.69	3.06E-04	2.87E-03^*	-0.19	Down
DLD	0.12	9.27	3.28	1.27E-03	8.67E-03^*	-1.50	Down
GLS	0.11	8.19	2.55	1 17E-02	4 47E-02^*	-3.50	Down

*p < 0.05

3.4 Pearson correlation coefficient analysis of cuproptosis genes associated with PE

In order to further study the relationship between the central gene and maternal blood pressure and related factors of maternal and fetal outcomes, Pearson correlation analysis was used to analyze and show that "NFE2L2", "PDHA1", "PDHB", "DLD", "GLS" were associated with peak blood pressure (systolic and diastolic), termination of pregnancy, placental weight, neonatal weight, etc. The results showed that the expression of "NFE2L2" was negatively correlated with the peak blood pressure (systolic and diastolic blood pressure), umbilical blood flow PI, placental weight [log2(fold change) = -2.14, p = 4.31e-03], neonatal weight, and gestational termination week, i.e., the peak blood pressure (systolic and diastolic blood pressure)[log2(fold change) = -7.87, p = 6.89e-06; log2(fold change) = -8.75, p = 2.71e-06], umbilical blood flow PI [log2(fold change) = -2.14, p = 4.31e-03] were negatively correlated, but positively correlated with placental weight [log2(fold change) = -6.68, p = 2.45e-05] (Figure 7A-7C), neonatal weight [log2(fold change) = -12.59, p = 4.74e-08] and gestational termination week [log2(fold change) = -1.12, p = 0.01] (Figure 7D-7F). Other cuproptosis genes were related to some maternal and fetal outcomes, such as "PDHA1" and "DLD", which were inversely proportional to peak blood pressure (systolic and diastolic blood pressure), and were proportional to placental weight and neonatal weight. However, there was no correlation between the termination week of pregnancy and umbilical blood flow PI (Figure S1-S2). "PDHB" was negatively correlated with peak blood pressure (systolic and diastolic blood pressure) and umbilical blood flow PI, meanwhile positively correlated with placental weight and neonatal weight, but it did not affect the termination of pregnancy (Figure S3). "GLS" was negatively correlated with peak systolic blood pressure and umbilical artery PI and positively correlated with placental weight, while there was no correlation in other aspects (Figure S4).

3.5 ROC curve analysis of the potential of cuproptosis gene as a predictor of PE

Based on the correlation between the above factors related to maternal and fetal outcomes and the cuproptosis genes, the results showed that the AUC values of "NFE2L2", "PDHA1", "PDHB", "DLD", and "GLS" were 0.759, 0.692, 0.686, 0.636, and 0.597, respectively, in terms of the potential to distinguish the markers of PE patients and normal pregnant women (Figure 8A-8E). These results pointed that "NFE2L2" was the most closely related cuproptosis gene to predict the occurrence and development of PE, followed by "PDHA1", "PDHB" and "DLD". While, "GLS" resulted as the least closely related to PE. To further clarify the potential influence of the above cuproptosis genes on the occurrence of PE, we conducted a RT-qPCR experiment, which showed that in addition to "GLS", the other four genes had significantly low expression in PE placenta, with a statistically significant difference (p < 0.05)(Figure 8F).

To the best of our knowledge, the correlation between CRGs and the development of PE has not been studied before. In the present study, we first analyzed the general situation of normal pregnant women and PE pregnant women in GSE data, finding that the BMI, systolic blood pressure, and diastolic blood pressure of PE pregnant women were significantly higher than those of normal pregnant women, and the termination of pregnancy occurred significantly earlier compared to normal pregnant women. On this basis, 2831 DEGs between normal placental tissue and PE placental tissue were screened in the database, including 1722 up-regulated genes and 1109 low expression genes. Combined with 19 genes related to cuproptosis in the current cancer database, including "NFE2L2", "NLRP3", "ATP7B", "ATP7A", "SLC31A1", "FDX1", "LIAS", "LIPT1", "LIPT2", "DLD", "DLAT", "PDHA1", "PDHB", "MTF1", "GLS", "CDKN2A", "DBT", "GCSH", and "DLST", it was found that cuproptosis genes have the same genes as differential genes, including "NFE2L2", "PDHA1", "PDHB", "DLD", "GLS". Therefore, we speculate that cuproptosis has an important role in regulating the occurrence of PE, which also indicates the potential role of cuproptosis in predicting PE.

Through hierarchical clustering, adjacency relationship, and heat map analysis, we found that the key genes of cuproptosis, "NFE2L2", "PDHA1", "PDHB", "DLD" and "GLS", had potential roles in the prediction and diagnosis of PE. In order to clarify that "NFE2L2", "PDHA1", "PDHB", "DLD" and "GLS" can potentially affect the maternal and fetal outcomes of PE, we associated the above gene expression differences with the maximum systolic blood pressure, maximum diastolic blood pressure, umbilical artery PI ratio, uterine artery PI ratio, the last week of pregnancy, and neonatal weight of pregnant women. Our results showed that "NFE2L2" was the cuproptosis gene most closely related to the occurrence of PE, whose up-regulated expression could significantly improve maternal and fetal outcomes. The other four cuproptosis genes were related to some maternal and fetal outcomes, suggesting that the increased expression of "PDHA1", "PDHB", "DLD" and "GLS" can improve maternal and fetal prognosis to varying degrees. Next, ROC curve analysis was used to determine that the fluctuations in the expression of "NFE2L2", "PDHA1", "PDHB", "DLD", and "GLS" could affect the occurrence and development of PE. In order to further determine that the above four cuproptosis genes could be used as predictive markers of PE for in-depth research, we used RT-qPCR to verify them in placental tissue. We found that "NFE2L2", "PDHA1", "PDHB", and "DLD", as screened cuproptosis related genes, could affect the progress of PE by regulating cuproptosis related mechanisms. However, "GLS" had no significant difference in the expression of PE placental tissue.

Many studies have proved that cuproptosis mediates cell death by regulating the TCA cycle. In this study, four cuproptosis genes (i.e., "NFE2L2", "PDHA1", "PDHB" and "DLD") with certain clinical significance were found to have different roles in cuproptosis[10]-[11]. The transcription factor encoded by "NFE2L2" - nuclear factor red cell 2 related factor 2 (NRF2) is a key regulator of antioxidants, which maintains homeostasis by detecting the level of oxidative stress in cells. In its inactive form, NRF2 combines with its negative regulator KEAP1. Yet, when reactive oxygen species (ROS) appear in cells, the cysteine residue of KEAP1 is modified, and KEAP2 is separated from NRF2. Then, NRF2 is transferred to the nucleus and acts as a transcription factor in combination with antioxidant response elements. It is currently recognized as a potential tumor therapeutic target[12][13]. "PDHA1" and "PDHB" are pyruvate dehydrogenase (PDH) complexes. These are nuclear-encoded mitochondrial multienzyme complexes that catalyze the overall conversion of pyruvate to acetyl coenzyme a and CO2 and provide the main link between glycolysis and the TCA cycle. The PDH complex is composed of multiple copies of three enzyme components: pyruvate dehydrogenase (E1), dihydroceramide acetyltransferase (E2), and thiamine dehydrogenase (E3). PDHA1 encodes E1 containing E1 active site α and has a key role in the function of the PDH complex. PDHB encodes E1 β Yaki[14][15]. Dihydrothiamide dehydrogenase (DLD) encodes a class I pyridine nucleotide disulfide oxidoreductase family member. As a mitochondrial protein, DLD has an important role in the energy metabolism of eukaryotes; it participates in at least five multi-enzyme complexes and is a necessary component for the complex to complete the reaction. In addition, DLD, as a flavoprotein oxidoreductase, uses FAD as a cofactor to receive protons and electrons to catalyze the formation of disulfide bonds[16]23]. These three genes all have an impact on the mitochondrial tricarboxylic acid cycle.

Our research has multiple advantages. First, this is the first study that explored the role of cuproptosis in the occurrence and development of PE. Cell death has always been a focus of interest in cancer research. Many studies have proved that cell death is the basis of tumor occurrence and development. Cuproptosis is a new type of cell death, which is different from other cell death mechanisms, as it mainly depends on mitochondrial respiration. In this study, we screened and compared the database and verified it in placental tissue using RT-qPCR technology. It was found that NFE2L2, PDHA1, PDHB, and DLD, as selected prognostic genes, can affect the progress of PE by regulating cuproptosis-related mechanisms.

However, the present study also has some limitations. First, although we screened NFE2L2, PDHA1, PDHB, and DLD genes through the screening of the database and the validation of clinical tissue samples, the number of tissue samples for validation was not large enough. Secondly, the specific regulatory molecular mechanisms of CRG in PE need to be further investigated.

To sum up, this study analyzed the genes that can regulate the progress of PE by combining the database data, along with the reported CRGs, and concluded that NFE2L2, PDHA1, PDHB, and DLD might have a key role in PE and further verified the clinical samples to provide a new direction for the follow-up study of the molecular mechanism of PE.

Author Contributions

Y.Zhang. takes responsibility for the content of manuscript, including the data and analysis. Conception and design: X.Tang and Y.Liu; development of methodology: X.Tang and Y.Liu; acquisition of data: Y.Zhang; analysis and interpretation of data: Y.Liu; writing—review and/or revision of the manuscript: X.Tang and Y.Zhang; administration: Y.Zhang; study supervision: X.Tang and Y.Zhang.All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the National Natural Science Foundation of China (grant No. 81801472), the Project of Natural Science Foundation of Jiangsu Province (grant No. BK20181080).

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

https://www.ncbi.nlm.nih.gov/geo. along with the accession number (GSE75010).

Ethics approval and consent to participate

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations. The Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (registration number: 2018-SR-252) has approved all the experiments. The informed consent was obtained from study participants.

Consent for publication

Not Applicable.

Conflicts of Interest

No conflicts of interest, financial or otherwise, are declared by the authors.

Acknowledgments

Not Applicable.

Author details

¹Department of Obstetrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China

Lafleur, N., et al., Association of Endometriosis and Severe Maternal Morbidity. Obstet Gynecol, 2022. 140(6): p. 1008-1016.
Bian, Z., R. Fan, and L. Xie, A Novel Cuproptosis-Related Prognostic Gene Signature and Validation of Differential Expression in Clear Cell Renal Cell Carcinoma. Genes (Basel), 2022. 13(5).
Tang, D., X. Chen, and G. Kroemer, Cuproptosis: a copper-triggered modality of mitochondrial cell death. Cell Res, 2022. 32(5): p. 417-418.
Zhang, G., J. Sun, and X. Zhang, A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma. Sci Rep, 2022. 12(1): p. 11325.
Xu, S., et al., Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma. Front Genet, 2022. 13: p. 938259.
Radzyminska-Lenarcik, E., K. Maslowska, and W. Urbaniak, Removal of Copper (II), Zinc (II), Cobalt (II), and Nickel (II) Ions by PIMs Doped 2-Alkylimidazoles. Membranes (Basel), 2021. 12(1).
Gao, W., et al., Elesclomol induces copper-dependent ferroptosis in colorectal cancer cells via degradation of ATP7A. Mol Oncol, 2021. 15(12): p. 3527-3544.
Li, S.R., L.L. Bu, and L. Cai, Cuproptosis: lipoylated TCA cycle proteins-mediated novel cell death pathway. Signal Transduct Target Ther, 2022. 7(1): p. 158.
Cobine, P.A. and D.C. Brady, Cuproptosis: Cellular and molecular mechanisms underlying copper-induced cell death. Mol Cell, 2022. 82(10): p. 1786-1787.
Chen, Y., et al., A broad cuproptosis landscape in inflammatory bowel disease. Front Immunol, 2022. 13: p. 1031539.
Zhou, C., et al., Regulation, genomics, and clinical characteristics of cuproptosis regulators in pan-cancer. Front Oncol, 2022. 12: p. 934076.
Sengoku, T., et al., Structural basis of transcription regulation by CNC family transcription factor, Nrf2. Nucleic Acids Res, 2022.
Chen, T., et al., Ryanodine receptor 2 promotes colorectal cancer metastasis by the ROS/BACH1 axis. Mol Oncol, 2022.
Yang, C., et al., Genome-Wide CRISPR/Cas9 Library Screening Revealed Dietary Restriction of Glutamine in Combination with Inhibition of Pyruvate Metabolism as Effective Liver Cancer Treatment. Adv Sci (Weinh), 2022. 9(34): p. e2202104.
Liu, H., Pan-cancer profiles of the cuproptosis gene set. Am J Cancer Res, 2022. 12(8): p. 4074-4081.
Tabolacci, C., et al., Identification of Dihydrolipoamide Dehydrogenase as Potential Target of Vemurafenib-Resistant Melanoma Cells. Molecules, 2022. 27(22).

No competing interests reported.

qpcr.xlsx
FigureS1.jpg
Figure S1: Linear regression model of PDHA1
FigureS2.jpg
Figure S2: Linear regression model of PDHB
FigureS3.jpg
Figure S3: Linear regression model of DLD
FigureS4.jpg
Figure S4: Linear regression model of GLS.

Download PDF

Journal Publication

published 10 Jan, 2024

Read the published version in BMC Pregnancy and Childbirth →

Editorial decision: Major revision
15 Sep, 2023
Reviews received at journal
15 Sep, 2023
Reviews received at journal
07 Aug, 2023
Reviewers agreed at journal
20 Jul, 2023
Reviewers agreed at journal
02 Jul, 2023
Reviewers agreed at journal
30 May, 2023
Reviewers invited by journal
18 May, 2023
Editor assigned by journal
18 May, 2023
Editor invited by journal
08 Feb, 2023
Submission checks completed at journal
08 Feb, 2023
First submitted to journal
13 Jan, 2023

You are reading this latest preprint version

A novel cuproptosis-related prognostic gene profiles in preeclampsia

Status:

Journal Publication

Version 1

Abstract

Figures

1. Background

2. Methods

3. Results

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1