Identification of key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and their relationship with immune cell infiltration using weighted gene co-expression network analysis and least absolute shrinkage and selection operator

doi:10.21203/rs.3.rs-2471794/v1

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Research Article

Identification of key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and their relationship with immune cell infiltration using weighted gene co-expression network analysis and least absolute shrinkage and selection operator

https://doi.org/10.21203/rs.3.rs-2471794/v1

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Background

The pathogenesis of focal segmental glomerulosclerosis (FSGS) is unclear, and diagnostic methods are limited. This study aimed to identify key biomarkers in the tubulointerstitium of FSGS patients and their association with immune cell infiltration.

Methods

The microarray expression and related data( GSE108112 and GSE200818) were collected from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Identification and enrichment analysis of differentially expressed genes (DEGs) was performed. Additionally, PPI networks of the DEGs were constructed and classified using Cytoscape plug-in MCODE. Weighted gene co-expression network analysis was used to identify the most critical gene modules. Least Absolute Shrinkage and Selection Operator regression data were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The major transcription factors affecting the hub genes were identified by Cytoscape plug-in iregulon. Infiltration of 28 immune cells and their interactions with hub genes were analyzed.

Results

In total, 535 DEGs were identified, including 219 upregulated genes and 316 downregulated genes. DEGs function mainly enriched in immune-related diseases and signaling fluxes. Cytoscape plug-in MCODE obtained nine modules with a total of 81 genes. The central module of WGCNA (green module, including 237 genes) in the correlation heap had the greatest association with the tubulointerstitial in FSGS. Three key genes (fractalkine/CX3C chemokine ligand 1 (CX3CL1), transforming growth factor beta 1 (TGFB1), and peroxisome proliferator activated receptor gamma coactivator 1 alpha (PPARGC1A)) were screened as potential tubulointerstitium biomarkers in FSGS. The transcription factor early growth response factor 1 (EGR1) had a regulatory effect on all three key biomarkers. Immune infiltration showed a significant correlation between CD4 + T cells, CD8 + T cells, and natural killer T cells. The results Infiltration of 28 immune cells showed that CX3CL1 and TGFB1 were enhanced, and PPARGC1A was decreased in immune and inflammation-related pathways.

Conclusions

The activation of natural killer T cells is closely related to tubulointerstitial renal lesions in FSGS. CX3CL1, TGFB1, and PPARGC1A may play important roles in the tubulointerstitium of FSGS through immune-related signaling pathways.

Urology & Nephrology

Immunology

Bioinformatics

focal segmental glomerulosclerosis

tubulointerstitium

immune cell infiltration

Weighted gene co-expression network analysis

Least Absolute Shrinkage and Selection Operator

CX3CL1

TGFB1

PPARGC1A

Nephrotic syndrome (NS) is a common manifestation of glomerular diseases. Focal segmental glomerulosclerosis (FSGS), as a pathological type of nephrotic syndrome, is often characterized by steroid resistance and can progress to end-stage renal failure(Shin et al. 2018). The prognosis of patients with FSGS after biopsy is poor, with a survival rate of 43.8%(Han et al. 2019). The pathological changes of FSGS include glomerular sclerosis, tubular atrophy and interstitial fibrosis in the involved renal segment(Agrawal et al. 2018; D'Agati et al. 2011). Repeated renal tubule injury leads to the loss of renal tubules, renal blood vessels, and tubulointerstitium, thus aggravating fibrosis(Grgic et al. 2012). Renal tubulointerstitial disease has a strong and independent relationship with FSGS, especially severe tubulointerstitial fibrosis, which is a risk factor for renal function decline in patients with FSGS(Alexopoulos et al. 2000; Tang et al. 2013). In clinical practice, some patients do not respond to immunosuppressive therapy, and develop a proinflammatory or pro-fibrotic environment associated with immune changes and disease progression, presenting with the pathological features of FSGS and poor prognosis (Kronbichler et al. 2016). Therefore, it is particularly important to explore the correlation between immune cell infiltration and FSGS pathology. Then, the diagnostic criteria for FSGS are limited. Therefore, understanding the key biomarkers of FSGS and their relationship with immune cell infiltration may help to develop effective diagnostic and therapeutic strategies.

With the advent of the technology era and mass development of publicly available high-throughput data worldwide, these data can be effectively used to comprehensively understand and analyze the molecular mechanisms of various diseases(Gao et al. 2021; Liang 2021). Bioinformatics analysis has been widely used in various clinical fields, especially the weighted gene co-expression network analysis (WGCNA) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, which have played important roles in various clinical applications (Climente-González et al. 2019; Zhao et al. 2010).

In this study, we used the microarray data of mRNA in the tubulointerstitium samples of FSGS patients and healthy donors to screen the key biomarkers related to FSGS tubulointerstitium by WGCNA and LASSO. To better understand the key biomarkers, we predicted their transcription factors and evaluated their relationship with immune cell infiltration, which may provide new targets for the diagnosis and treatment of FSGS.

2.1 Data Collection

The microarray expression and related data of GSE108112 and GSE200818 were collected from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/).110 samples were collected, including 100 in the tubulointerstitial FSGS group and 10 healthy controls. The GSE133288 dataset used for validation consisted of 74 samples, including 69 tubulointerstitial FSGS samples and five control samples. The sequencing platform used was GPL19983.

2.2 Identification of Differently Expressed Genes (DEGs)

Datasets GSE108112 and GSE200818 were annotated with “sva”. The "Limma" package of R software (version 4.1.3) was used to filter the data and determine the DEGs. Data standardization and adjusted p-value (adj.p.Val) < 0.05, and log fold change (log FC) > 1 was chosen based on the previous studies(Davis & Meltzer 2007; Ritchie et al. 2015)

2.3 Functional enrichment of DEGs

Gene oncology (GO), Kyoto Encyclopedia of Genes and Genomes(KEGG) and Gene set enrichment analysis (GSEA) were the three parts for functional enrichment of DEGs. GO and KEGG enrichment used “org.Hs.eg.db”, “cluster Profiler”, “enrichplot”, and “ggplot2” in the R software package(Zhang et al. 2019). the circle diagram of GO and KEGG were drawed with”circlize”, “RcolorBrewer”, “dplyr”, “ComplexHeatmap” package of R software.GSEA enrichment used “limma”,“org.Hs.eg.db”, “cluster Profiler”, “enrichplot” in the R software package. The annotated gene set entitled “immunesigdb” used in GSEA was downloaded from Molecular Signatures Database (MsigDB)(Godec et al. 2016).

2.4 Protein-protein interactions (PPI) network construction and submodule analysis

The online database STRING(Szklarczyk et al. 2015) was used to construct a PPI network of DEGs. In addition to exploring the relationships among the DEGs, a confidence score > 0.4 was set as significant. Next, Cytoscape 3.8.0 (obtained from https://www.cytoscape.org (Shannon et al. 2003) was used to visualize the PPI network and perform further structural analysis. The Molecular Complex Detection (MCODE) plug-in (Bader & Hogue 2003) was used to detect clustering, and modules with the following characteristics were considered significant: MCODE score > 3, degree cutoff value = 2, node score cutoff value = 0.2, k-score = 2, maximum depth = 100, number of nodes ≥ 10. The Cytoscape plug-in Cyto-Hubba(Wang et al. 2021) was used to calculate the hub genes. The pivot gene ranks first in each MCODE module with "degree" as the calculation method.

2.5 Construction of weighted co-expression network

The "WGCNA" package of R software was used to construct a coexpression network of DEGs(Langfelder & Horvath 2008). We established an adjacency matrix, defined a correlation power (soft thresholding parameter(β)) showing strong relations between genes and penalizing the weak correlation. Then we converted the adjacency into a topological overlap matrix (TOM) to measure the network connectivity of genes. the TOM summed up the adjacent genes for the network gene ratio and calculated the corresponding dissimilarity. We used average linkage hierarchical clustering based on TOM dissimilarity measurement to classify genes showing similar expression profiles with gene modules, which were represented by branches and different colors of the cluster tree, constructed module relationships, calculation of the correlation between gene modules and phenotypes, and the modules related to clinical traits were identified. The total modules were identified. The number of dynamically spliced genes per module was 60. the hub genes for each module were identified by module membership (MM) > 0.6 and gene significance (GS) > 0.3.

2.6 Identification of the hub gene

Using a "venn" package of R software, we obtained the intersection genes between the hub genes of the highest correlation module in WGCNA and the hub genes of core module of the cytoscape plug-in MOCDE. We used the "glmnet"of R package to cross the intersection genes and used the LASSO algorithm to determine the final hub genes(Engebretsen & Bohlin 2019).Boxplots were used to assess hub gene upregulation and downregulation levels in the tubulointerstitium of FSGS and healthy individuals. Receiver operating characteristic curves were drawn and their AUCs determined to evaluate the diagnostic value of the key genes using the pROC package in R. Genes with AUC > 0.85 were defined as hub genes. Using a separate dataset (GSE133288), we examined the expression levels of genes and diagnostic value of the hub genes. The GSE133288 was tested mainly from boxplot, ROC and their AUCs for the diagnostic value.

2.7 Identification of transcription factors acting on hub genes

Major factors affecting hub genes were identified, mainly TFs, and the plug-in regulon was used to detect potential TFs of hub genes(Bayat et al. 2021). In this study, TFs with a normalized enrichment score (NES) > 3 were considered statistically significant(Bayat et al. 2021).

2.8 Evaluation of immune cell infiltration Its Correlation With Hub Genes

Single-sample gene set enrichment analysis (ssGSEA) was applied to quantify the relative infiltration levels of 28 immune cells in the GSE108112 and GSE200818 datasets. The differences in the expression levels of 28 types of immune-infiltrating cells were presented for a vioplot using a "vioplot" package of R software. Finally, we calculated the correlation between the three central genes and immune-infiltrating cells with “tidyverse” (Mangiola et al. 2021) and “reshape2” (Feng et al. 2022).then we used the "ggplot2" R software package for visualization.

2.9 Statistical analysis

All statistical tests and bioinformatics analysis were conducted by R (versions 4.1.3). According to the data type and characteristics, comparisons were made using the Mann–Whitney U test for categorical variables and Student’s t-test for continuous variables. All statistical analyses accepted p < 0.05 for two-sided tests as statistically significant.

3.1 Identification of DEGs

According to the adjusted P-value < 0.05, and | log FC |>1, 535 DEGs were obtained, that included 219 upregulated genes and 316 downregulated genes. DEGs were displayed on a heatmap (Fig. 1a) and volcano map (Fig. 1b).

3.2 Functional analysis of GO and KEGG pathway of DEGs

GO and KEGG pathway enrichment analyses were performed to determine the functional characteristics and signaling pathways of DEGs associated with FSGS. As relevant aspects of biological function, GO enrichment analysis revealed that DEGs were mostly enriched in immunoglobulin mediated immune response, B cell mediated immunity, positive regulation of leukocyte activation, positive regulation of cell activation, and humoral immune response (Fig. 2a, b).

KEGG signaling pathway analysis revealed that DEGs were primarily enriched in the complement and coagulation cascades, asthma, pertussis, Staphylococcus aureus infection, systemic lupus erythematosus, and AGE-RAGE signaling pathways in diabetic complications (Fig. 3a, b).

3.3 PPI network construction and submodule analysis

A PPI network of 535 DEGs was constructed using the STRING database to study the interactions amongst the DEGs. After hiding the disconnected nodes with confidence > 0.4, 268 nodes and 1115 edges were involved in the PPI network. The Cytoscape plug-in MCODE was used to classify the PPI network modules. Nine significant modules and 81 core genes were identified (Table 1). Cyto-hubba was then used to score the top gene in each module. The degree algorithm in the cyto-hubba plug-in was applied to select the top nine hub genes (Fig. 4). These results indicated that Lymphocyte cytosolic protein 2 (LCP2), complement C1qa (C1QA), interferon (IFN)-induced transmembrane protein 1 (IFITM1), histone cluster 1, H4i (HIST1H4L), transferrin receptors (TFRC), peroxisome proliferator activated receptor gamma, coactivator 1 alpha (PPARGC1A), C-C chemokine ligand 5 (CCL5), c-fos gene (also known as FOS), and solute carrier family 19 member 2 (SLC19A2) contribute to the tubulointerstitial of FSGS.

Table 1

Nine modules and 81 key genes determined by MCODE.
Cluster	Score (Density*#Nodes)	Nodes	Edges	Node IDs
1	12.8	16	96	C1QC, IL10RA, TYROBP, LCP2, CTSS, MPEG1, PTPRC, CYBB, RNASE6, C1QB, TLR7, FCER1G, MNDA, HCST, LAPTM5, CD53
2	5.385	14	35	C1QA, IL2RG, DOCK2, CD48, C1R, CD52, IKZF1, C1S, CD2, LCP1, C2, CFB, LYZ, GZMA
3	5	5	10	IFI6, OAS3, IFITM2, IFIT5, IFITM1
4	4.4	11	22	ALB, HIST1H3F, F3, HIST1H2AM, HIST1H3I, HIST1H2BK, FGA, CX3CL1, CCL21, HIST1H4L, HIST1H3D
5	3.857	15	27	DUSP1, FOSB, LDLR, PLTP, APOC1, TFRC, LPL, ZFP36, TGM2, EGF, APOC3, LCN2, TGFB1, PROM1, RGS2
6	3.6	6	9	NR4A2, PCK1, G6PC, UCP2, NR4A1, PPARGC1A
7	3.6	6	9	FN1, CCL19, CXCL16, SAA1, CCL5, CXCL6
8	3.5	5	7	SERPINE1, MMP7, EGR1, FOS, REN
9	3	3	3	SLC19A2, SLC20A1, SLC25A33

3.4 Co-expression network construction and hub module identification

14491 genes were selected to construct WGCNA. Soft level value β= 2 (R^2=0.84, slope=−2.61) was consistent with the topological relations of the scale-free networks (Figure 5). A one-step co-expression matrix was created, and five gene modules were obtained by dynamic hybridization and splicing (Figure 6 a). The central module (green module, including 237 genes) in the correlation heap had the greatest association with tubulointerstitial FSGS (cor=0.87; p=8e-16) (Figure 6 b, c). There was a strong link between GS and MM in the green module (cor = 0.49; p=2.4e-48) (Figure 6 d). Therefore, the green module was used as the central module for subsequent analysis.

3.5 Identification of DEGs and screening of hub genes

The Cytoscape plugin MCODE was used to determine 81 key genes. The intersection of these 81 key genes and the 237 WGCNA genes resulted in three overlapping genes (fractalkine/CX3C chemokine ligand 1 (CX3CL1), transforming growth factor beta 1 (TGFB1), and peroxisome proliferator activated receptor gamma, coactivator 1 alpha (PPARGC1A)) (Fig. 7a). After LASSO analysis of these three genes, the following hub genes were identified: CX3CL1, TGFB1, and PPARGC1A (Fig. 7b, c).

3.6 Detection and diagnostic value of hub gene expression levels

The expression levels of the three hub genes were verified using a box graph. The expression level of PPARGC1A (p < 0.001) in the tubulointerstitial FSGS group was significantly lower than that in the control group. CX3CL1 and TGFB1 levels (p < 0.0001) were significantly higher than those in the control group. The expression levels of the three key markers were verified using the different dataset GSE133288 (Fig. 8a, b).

The ROC curve was used to analyze the area under the curve (AUC) value of the three hub genes to evaluate their sensitivity and specificity for the diagnosis of FSGS. The AUC value of the three genes > 0.85 indicated that these genes have a high diagnostic value for FSGS (Fig. 9a,b). To determine clinical applicability, the diagnostic value of the three key markers was verified using the GSE133288 dataset.

3.7 Transcriptional factor regulatory network of hub genes

The major transcription factors (TFs) affecting the hub genes were identified and the network regulation was mapped considering TFs and target genes with an NES > 3 (Fig. 10).

3.8 Immune cell infiltration and its link with hub genes

The differences in immune cell infiltration between the tubulointerstitial FSGS and control groups were tested using ssGSEA (Table 2). The scatterplot of 28 immune cells in the GSE108112 and GSE200818 datasets was shown in Fig. 11a. Immune cell infiltration analysis showed that the infiltration of natural killer T cells, gamma delta T cells, effector memory CD4 T cells, type 1 T helper cells, activated CD8 T cells, and eosinophils in the tubulointerstitial FSGS group was significantly higher than that in the control tissues. These results indicate that these cells play a key role in the tubulointerstitium of FSGS (Fig. 11b). The link between the 28 immune cells and hub genes indicated strong interactions of PPARGC1A with monocyte cells, effector memory CD4 T cells, natural killer T cells, and neutrophils (p < 0.001); CX3CL1 with natural killer cells, effector memory CD4 T cells, central memory CD4 T cells, and type 1 T helper cells (p < 0.001); TGFB1 with natural killer cells, type 1 T helper cells, effector memory CD4 T cells, and activated CD8 T cells (p < 0.001) (Fig. 11c). This outcome validates the main factor of immune cell infiltration in the tubulointerstitial of FSGS.

Table 2

Ranking of 28 immune cells by p-value
Cell	P-value
Natural killer T cell	3.81E-06
Gamma delta T cell	2.16E-05
Effector memory CD4 T cell	6.39E-05
Type 1 T helper cell	9.45E-05
Activated CD8 T cell	0.000157
Eosinophil	0.000171
Natural killer cell	0.000202
CD56dim natural killer cell	0.000315
Monocyte	0.000341
Activated dendritic cell	0.001338
Effector memory CD8 T cell	0.001388
Regulatory T cell	0.001438
Macrophage	0.005957
T follicular helper cell	0.007894
Activated B cell	0.008923
MDSC	0.009199
Plasmacytoid dendritic cell	0.01751
Immature B cell	0.029385
Central memory CD4 T cell	0.037097

Note: The cells are ordered by smallest to largest p-value

3.9 GSEA enrichment analysis of DEGs

To discover the underlying mechanism of immune function in the tubulointerstitium of FSGS, we used a set of immune characteristic genes from the MsigDB database as a reference for GSEA of DEGs. The results showed that the gene sets of each group were especially gathered in CD4 + T cells, B cells, and PBMC. The results reveal the role of immune-related genes in the tubulointerstitium of FSGS (Fig. 12).

In this study, three key biomarkers, CX3CL1, TGFB1, and PPARGC1A, were identified and validated, and all three were shown to have diagnostic value. Interestingly, the transcription factor early growth response factor 1 (EGR1) had a regulatory effect on all three key biomarkers.

C-X3-C Motif Chemokine Ligand 1 (CX3CL1), also known as fractalkine, is mainly produced by glomerular endothelial cells and the tubular epithelium and can also be detected in podocytes, mesangial cells, renal tumor cells, and stromal cells (Chang & Chen 2020). Recent studies have shown that CX3CL1 is closely related to the pathogenesis of FSGS but is mainly expressed in the tubulointerstitium (Zhu et al. 2022). Although the above study suggests that this gene is expressed in the tubulointerstitium, it indicates that it is an FSGS-related gene, supporting the results of this study.

Transforming growth factor beta 1 (TGF-β1) has an important role in the process of renal fibrosis, manifested by inhibiting the proliferation of most cells, including glomerular epithelial cells, endothelial cells, and tubular epithelial cells (Border & Noble 1994). Moreover, it is present not only in the tubulointerstitium but also in the tubulointerstitium. There is strong theoretical support for TGF-β1 as a key biomarker for tubulointerstitial fibrosis in FSGS. For example, focal tubulointerstitial fibrosis is thought to result from glomerular scar formation in FSGS. Our results were consistent with those of the previous study.

Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α) is encoded by PPARGC1A and belongs to the PGC-1 family. PGC-1α is a major regulator of mitochondrial biogenesis, coordinating transcriptional mechanisms and resulting in increased mitochondrial mass, thus enabling tissues to adapt to increased energy demands (Li et al. 2017). Tran et al. (Tran et al. 2016)developed an inducible transgenic mouse model of renal tubular epithelium in which PGC-1α is specifically overexpressed in renal tubular epithelial cells.

Recent studies have shown that angiotensin-converting enzyme 2 (ACE2) expression is also associated with interstitial fibrosis and tubular atrophy in men and correlates with TGF-β1 in patients with FSGS studied by the Nephrotic Syndrome Research Network using renal tubular interstitial microarray data and clinical variables(Maksimowski et al. 2021). The results of this study were consistent with those of the present study. TGF-β1 acts as a key mediator of the reduction in fatty acid oxidation through Smad3-mediated repression of the PPARGC1A gene(Kang et al. 2015). TGF-β1 has a regulatory effect on PPARGC1A, suggesting a regulatory relationship between them. Among the MCODE modules, PPARGC1A was the core gene of one of the central modules, indicating that PPARGC1A also has a strong diagnostic value. CX3CL has been used as a biomarker for podocytes in relevant biological FSGS, although tissue expression varies, which is consistent with the findings of this study; it can be used as a reference standard (Zhu et al. 2022).

EGR1 acts as a major transcription factor for all three key biomarkers and plays a regulatory role for all of them. Transcription factors were also enriched in the AGE-RAGE signaling pathway in diabetic complications, and there was a strong relationship between EGR1, key biomarkers, and the AGE-RAGE signaling pathway in diabetic complications.

The main gene set in GSEA was enriched in CD4 + T cells, B cells, and PBMC. ssGSEA showed significantly higher infiltration of natural killer T cells in the tubulointerstitial FSGS group compared with that in control tissues. The murine invariant natural killer T (INKT) cells are unconventional lymphocytes that can rapidly secrete different cytokines through TCR stimulation to regulate the outcome of different immune-mediated diseases (Matsuda et al. 2008). Early studies have shown that INKT cell-derived cytokines can activate several other cell types, including NK cells, conventional CD4 and CD8 T cells, macrophages, and B cells, and recruit myeloid dendritic cells(Kronenberg & Gapin 2002). It can be concluded that the results of GSEA enrichment analysis correlated with natural killer T cell regulation. Animal experimental studies have shown that the INKT agonist, Giberellic Acid-Stimulated Like-1 (GSL-1), modulates pathogenesis of adriamycin-induced glomerulosclerosis, with results that are consistent with those of the present study (Pereira et al. 2012). All three key biomarkers significantly correlated with natural killer T cells. Therefore, the results suggest that natural killer T cell activation is closely related to FSGS tubulointerstitial lesions.

We identified the relationship between biomarkers and immune cell infiltration in the tubulointerstitium of patients with FSGS by using a machine learning approach. Dual-specificity protein phosphatase1 (DUSP1) and nuclear receptor 4A1 (NR4A1) have been identified as potential sensitive biomarkers for the diagnosis of FSGS. Activated mast cells have a decisive effect on the occurrence and development of FSGS through tubular lesions and tubulointerstitial inflammation, and they are expected to become therapeutic targets for FSGS (Bai et al. 2022). While we used machine learning to analyze the differences between normal and renal tubular FSGS samples of GSE108112, GSE133288 and GSE121211 in our previous study, this study used WGCNA combined with LASSO to screen normal and renal tubular interstitial FSGS samples of GSE108112, GSE200818 samples. Therefore, the discrepancies in the results are most likely attributed to the different samples and research methods used in these studies.

This study had some limitations. The two datasets used contained different FSGS patients and controls, which may be reflected in the results. In addition, because the data we analyzed were obtained from public databases, further experiments are needed to validate these findings.

The activation of natural killer T cells is closely related to renal tubulointerstitial lesions in FSGS. Key biological markers (CX3CL1, TGFB1, and PPARGC1A) may play an important role in the tubulointerstitium of patients with FSGS through immune-related signaling pathways.

DEG

differentially expressed genes

FSGS

focal segmental glomerulosclerosis

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

LASSO

least absolute shrinkage and selection operator

Ethical Approval

GEO belongs to public databases. The patients involved in the database have obtained ethical approve. Users can download data for free for research and publish relevant articles. Our study is based on open source data. So there are no ethical issues and other conflicts of interest.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

EnLai Dai designed and conceived the study. YunXia Zhang and JunYuan Bai collected and analyzed the data, and drafted the manuscript. XiaoWei Pu and Juan Lv helped in the data analyses and revised the manuscript. All authors have reviewed and approved the manuscript for publication.

Funding

This work was supported by the National Science Foundation of China (82160852).

Availability of data and materials

In this study, publicly available datasets were analyzed. The GSE108112, GSE200818, and GSE133288 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).

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Identification of key biomarkers in the tubulointerstitium of patients with focal segmental glomerulosclerosis and their relationship with immune cell infiltration using weighted gene co-expression network analysis and least absolute shrinkage and selection operator

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Abstract

Background

Methods

Results

Conclusions

Figures

1. Background

2. Methods

2.1 Data Collection

2.2 Identification of Differently Expressed Genes (DEGs)

2.3 Functional enrichment of DEGs

2.4 Protein-protein interactions (PPI) network construction and submodule analysis

2.5 Construction of weighted co-expression network

2.6 Identification of the hub gene

2.7 Identification of transcription factors acting on hub genes

2.8 Evaluation of immune cell infiltration Its Correlation With Hub Genes

2.9 Statistical analysis

3. Results

3.1 Identification of DEGs

3.2 Functional analysis of GO and KEGG pathway of DEGs

3.3 PPI network construction and submodule analysis

3.4 Co-expression network construction and hub module identification

3.5 Identification of DEGs and screening of hub genes

3.6 Detection and diagnostic value of hub gene expression levels

3.7 Transcriptional factor regulatory network of hub genes

3.8 Immune cell infiltration and its link with hub genes

3.9 GSEA enrichment analysis of DEGs

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Supplementary Files

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