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. 2024 Mar 11;21(3):e14771. doi: 10.1111/iwj.14771

Identification and clinical validation of the role of anoikis‐related genes in diabetic foot

Nan Su 1,2, Jiwei Wang 1,2, Hengrui Zhang 1,2, Haoyong Jin 1,2, Baojian Miao 1,2, Jiangli Zhao 1,2, Xuchen Liu 1,2, Chao Li 1,2, Xinyu Wang 1,2,✉, Ning Yang 1,2,✉
PMCID: PMC10928261  PMID: 38468369

Abstract

This study aims to investigate the role of anoikis‐related genes in diabetic foot (DF) by utilizing bioinformatics analysis to identify key genes associated with anoikis in DF. We selected the GEO datasets GSE7014, GSE80178 and GSE68183 for the extraction and analysis of differentially expressed anoikis‐related genes (DE‐ARGs). GO analysis and KEGG analysis indicated that DE‐ARGs in DF were primarily enriched in apoptosis, positive regulation of MAPK cascade, anoikis, focal adhesion and the PI3K‐Akt signalling pathway. Based on the LASSO and SVM‐RFE algorithms, we identified six characteristic genes. ROC curve analysis revealed that these six characteristic genes had an area under the curve (AUC) greater than 0.7, indicating good diagnostic efficacy. Expression analysis in the validation set revealed downregulation of CALR in DF, consistent with the training set results. GSEA results demonstrated that CALR was mainly enriched in blood vessel morphogenesis, endothelial cell migration, ECM‐receptor interaction and focal adhesion. The HPA database revealed that CALR was moderately enriched in endothelial cells, and CALR was found to interact with 63 protein‐coding genes. Functional analysis with DAVID suggested that CALR and associated genes were enriched in the phagosome component. CALR shows promise as a potential marker for the development and treatment of DF.

Keywords: anoikis, bioinformatics analysis, CALR, diabetic foot, endothelial cell

1. INTRODUCTION

Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels. 1 The global incidence of diabetes is increasing significantly and reached approximately 537 million cases in 2021, according to the International Diabetes Federation (IDF). 2 , 3 Among the numerous complications associated with diabetes, diabetic foot is highly prevalent and serious. 4 It results from neuropathy and vascular lesions caused by lower extremity tissue damage, which can lead to ulceration, infection, tissue necrosis, and even amputation. 5 , 6 Studies have indicated that hyperglycaemia plays a pivotal role in the development of microvascular diabetic complications. Intracellular hyperglycaemia can disrupt blood flow, increase vascular permeability and reduce the production of nutritional factors by endothelial and nerve cells. 7 Vascular injury resulting from hyperglycaemia is considered a major factor in severe diabetic complications, including diabetic foot ulcer (DFU). 8 , 9 However, the current treatment methods for DFU, including debridement, wound dressings, anti‐infection treatments, peripheral vascular lesion treatment, strict blood glucose control and amputation, have not yet yielded satisfactory clinical outcomes. 10 , 11 Although these challenges exist for clinicians and researchers, they also present opportunities for further exploration of the pathological mechanisms of diabetic foot and the discovery of new therapeutic strategies and drug targets.

Anoikis is a distinctive form of programmed cell death that occurs when cells detach from their extracellular matrix. 12 Its primary role is to prevent abnormal cell growth and adhesion to an abnormal extracellular matrix. 13 Anoikis is crucial for tissue homeostasis and development and is involved in the regulation of diseases, such as metastatic cancer, cardiovascular disease, and diabetes. 14 Dobler reported that endothelial cells undergo shedding and anoikis in high glucose environments. 8 Zhang et al. discovered that dihydroartemisinin induces the anoikis of endothelial cells by activating the JNK signalling pathway, leading to tumour angiogenesis disorders. 15 Previous research has shown a correlation between diabetic foot ulcers, neuropathy, vasculopathy, increased endothelial cell detachment and premature death caused by anoikis. 8 Hence, anoikis may play a crucial role in the occurrence and progression of diabetic foot.

Currently, research on the diagnostic value of anoikis‐related genes in diabetic foot is limited. However, the identification of such genes associated with diabetic foot provides an opportunity to explore and develop new diagnostic and therapeutic targets. Therefore, in this study, we performed bioinformatics analysis of differentially expressed genes (DEGs) comparing diabetic foot samples and healthy tissue samples. We conducted various functional enrichment analyses and employed two machine learning algorithms to identify characteristic genes for predicting disease incidence. Furthermore, the differential expression of these characteristic genes was validated in an external dataset, after which functional analysis was performed. Through our research, we aim to gain a deeper understanding of the pathogenesis of diabetic foot and the role of anoikis‐related processes in its progression. Figure 1 depicts the flowchart of our study, which provides a solid foundation for elucidating the potential molecular mechanisms.

FIGURE 1.

FIGURE 1

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The workflow of data preparation, processing, analysis and validation.

2. MATERIALS AND METHODS

2.1. Data collection

The GSE7014, GSE80178 and GSE68183 Series Matrix data files were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) on platform GPL57016 and GPL16686. A total of 54 sets of transcriptome data were used as the training set. This set included 12 control patients (n = 12) and 42 DF patients (n = 42). Expression data from previous gene sequencing performed by our group were utilized as the validation dataset, which consisted of 10 control samples (n = 10) and 10 DF samples (n = 10). Anoikis‐related genes (ARGs) were obtained from the GeneCard database (https://www.genecards.org/) and Harmonizome (https://maayanlab.cloud/Harmonizome/).

2.1.1. Anoikis‐related DEG screening

The analysis of differences in gene expression data was assessed using Limma, 16 which employs linear models and Bayesian methods. In this study, we applied the ‘Limma’ R package to analyse the differential expression of genes related to anoikis in the training set. The criteria for identifying differentially expressed genes were based on a p value threshold of less than 0.05 at the gene expression level. Visualization of the DEGs and their correlations was performed using the ‘pheatmap’ and ‘corrplot’ packages respectively.

2.2. Functional enrichment analysis of anoikis‐related DEGs

To comprehensively explore the functional role of genes related to anoikis, we performed functional annotation using the R package ‘clusterProfiler’. Gene ontology (GO) 17 is a standardized resource that describes gene and protein functions based on the categories of molecular function, cellular component and biological process. For a detailed understanding of cellular and molecular networks and biochemical pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) 18 database. The significance thresholds for GO and KEGG enrichment analysis were set at p < 0.05 and corrected p < 1.

2.3. Machine learning for biomarker identification

In this study, we employed Lasso logistic regression and the SVM‐RFE algorithm to perform feature selection for the identification of biomarkers associated with diabetic foot. Initially, we utilized the ‘glmnet’ package in R language for Lasso analysis, which selects characteristic genes from all DEGs through binomial logistic regression. The optimal penalty parameter λ, which was determined by minimizing the binomial deviation, was also determined. Subsequently, the SVM package was employed to implement the support vector machine recursive feature elimination (SVM‐RFE) model with a 10‐fold cross‐validation strategy. Furthermore, we compared the feature genes selected by the two algorithms and visualized their relationship using the ‘VennDiagram’ package in R, which identified the shared feature genes.

2.4. Verification of the expression and value of biomarkers

To evaluate the predictive value of these characteristic genes in the training set, the ‘pROC’ package in R was utilized for drawing the ROC curve and calculating the area under the curve (AUC). Simultaneously, the ‘glm’ package in R was employed to construct a logistic regression model using the combination of six key differentially expressed genes (hub DE‐ARGs) to predict sample classifications within the training set. The effectiveness of the model was subsequently verified through analysis of the ROC curve. Furthermore, the machine learning‐based identification of biomarkers was cross‐validated using an external dataset derived from previous gene sequencing done within our research group.

2.5. Single GSEA

The samples were grouped based on the expression of the target gene. The logFC (logarithm of fold change) for both the high and low‐expression groups was calculated, and the genes were sorted accordingly. GSEA was performed on these groups using the ‘clusterProfiler’ package, and the gene sets ‘c2.cp.kegg.symbols.gmt’ and ‘c5.go.symbols.gmt’ from the Molecular Signatures Database (MSigDB) were downloaded. The pathways and genes in this gene set served as references, and significantly enriched pathways and biological processes were determined using a significance level of p < 0.05 and p.adjust <0.05.

2.6. Databases

The Human Protein Atlas (HPA) database (http://www.proteinatlas.org) is a valuable resource for human protein information. Its primary goal is to provide information on the distribution of human proteins at both the tissue and cellular levels by offering free query functions to the general public. The HPA utilizes specialized antibodies and immunohistochemical techniques to investigate the distribution and expression of each protein in normal and tumour tissues, cell lines and blood cells. The results are presented through immunohistochemical staining maps, which are meticulously evaluated and annotated by experts. In this study, the hub genes were input to gain insights into their expression and interaction with other proteins in the cell line. The DAVID database (https://david.ncifcrf.gov/home.jsp) was accessed, and its ‘Start Analysis’ module under the ‘Upload’ section was specifically utilized. By inputting the hub genes and interacting protein genes stepwise, the database enables functional enrichment analysis of these genes.

2.7. Data analysis

Data processing and analysis were conducted using Microsoft Excel and R software (version 4.0.2). The nonpaired Student's t test was employed to compare two groups exhibiting a nonnormal distribution, with statistical significance defined as p < 0.05. Furthermore, the Pearson correlation coefficient was utilized to assess the correlation among anoikis‐related genes, where * denotes p < 0.05, ** denotes p < 0.01 and *** denotes p < 0.001.

3. RESULTS

3.1. DE‐ARG identification

In this study, we retrospectively analysed the data from 42 DF samples and 12 normal samples in the merged dataset. Subsequently, the difference analysis was performed using the ‘limma’ package, with a significance threshold of p < 0.05 for filtering. In total, 74 DE‐ARGs were identified, including 34 significantly upregulated genes and 40 significantly downregulated genes (Figure 2A). The correlations among these genes are depicted in Figure 2B.

FIGURE 2.

FIGURE 2

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Expression levels of DF‐ARGs in DM and DF and functional analyses of the DF‐ARGs. (A) Heatmap showing the expression patterns of the DF‐ARGs across samples. (B) Pearson correlation coefficients of these genes. (C–E) GO enrichment and (F, G) KEGG analyses indicated that the DF‐ARGs were significantly associated with the apoptosis, positive regulation of MAPK cascade, anoikis, focal adhesion, proteoglycans in cancer and the PI3K‐Akt signalling pathway, among others (Fisher's test).

3.2. Functional analysis of DE‐ARGs

The ‘clusterProfiler’ package in R was utilized to conduct GO functional and KEGG pathway enrichment analyses of the 74 DE‐ARGs. The analysis results were visually represented. GO enrichment analysis yielded a total of 1296 functional items, including 1146 biological processes, 62 cell components and 88 molecular functions. The top 10 significantly enriched items were visualized for each process (Figure 2C–E). The biological process analysis revealed significant enrichment in positive regulation of MAPK cascade, regulation of autophagy, stem cell differentiation, regulation of apoptotic signalling pathway, macroautophagy, morphogenesis of a branching structure, regulation of intrinsic apoptotic signalling pathway, anoikis, regulation of DNA biosynthetic process and positive regulation of miRNA transcription. Additionally, the cell composition analysis indicated enrichment in focal adhesion, cell−substrate junction, actin cytoskeleton, membrane raft, membrane microdomain, RNA polymerase II transcription regulator complex, platelet alpha granule, platelet alpha granule lumen, ESCRT I complex and the CD40 receptor complex. In terms of molecular function, the significantly enriched functions included DNA − binding transcription factor binding, DNA − binding transcription activator activity (RNA polymerase II − specific), DNA − binding transcription activator activity, protein serine/threonine kinase activity, protein serine kinase activity, cadherin binding, phosphatase binding, protein tyrosine kinase activity and collagen binding and MAP kinase kinase activity. The KEGG pathway enrichment analysis revealed that 86 signalling pathways were primarily associated with proteoglycans in cancer, the PI3K‐Akt signalling pathway, the MAPK signalling pathway, pathogenic Escherichia coli infection, apoptosis, Epstein–Barr virus infection, focal adhesion, lipid and atherosclerosis, microRNAs in cancer, hepatitis C and other signalling pathways (Figure 2F,G).

3.3. Identification of DF‐related characteristic DE‐ARGs

In this study, two machine learning algorithms were utilized to identify biomarkers of diabetic foot (DF). First, the LASSO regression algorithm was employed to screen for DE‐ARGs. By selecting the lambda.min parameter, the following 13 characteristic genes were identified: CALR, HGF, SIK2, KDR, CD36, ARHGEF7, TSC2, MALAT1, LDHA, NDRG1, OCLN, S100A7 and HTRA2 (Figure 3A,B). Then, the following 14 characteristic genes among the DEGs were identified through cross‐validation using the SVM‐RFE algorithm: NDRG1, CD36, SIK2, KDM3A, CALR, COPS5, EZR, TSC2, TSG101, GLO1, STK38, SMAD4, S100A7 and SH3GLB1 (Figure 3C,D). Finally, by comparing the results from the two machine learning algorithms, the following six key diagnostic biomarker genes were identified: CALR, SIK2, CD36, TSC2, NDRG1 and S100A7 (Figure 3E).

FIGURE 3.

FIGURE 3

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Six hub DF‐ARGs were identified as diagnostic genes for the progression of DF, and the six hub DF‐ARGs were used as tools for the differentiation of individuals without DF and those with DF. (A, B) LASSO logistic regression, with penalty parameter tuning conducted by 10‐fold cross‐validation, was used to select feature genes. (C, D) The SVM algorithm was utilized for feature gene selection. (E) Venn diagram showing the feature genes identified by both the LASSO and SVM algorithms. (F) ROC curves for the three individual marker genes from the merged dataset. (G) Logistic regression model for determining the AUC of differentiation between disease samples from the merged dataset using the combination of three DF‐ARGs.

Based on ROC curve analysis (Figure 3F) using the training dataset, the AUC of CALR was 0.787, that of SIK2 was 0.817, that of CD36 was 0.881, that of TSC2 was 0.705, that of NDRG1 was 0.740 and that of S100A7 was 0.718. After establishing a logistic regression model using the R ‘glm’ package, the AUC value for the combination of the six hub DE‐FRGs in the training dataset was 1.000 (Figure 3G). This indicates that the model accurately distinguishes between normal and DF samples on the basis of a single gene.

To validate the accuracy and reliability of the results, the expression levels of the six characteristic genes were determined using our own group's previous gene expression dataset. The results showed no difference in the expression of the SIK2, CD36, TSC2, NDRG1 and S100A7 genes (Figure 4B–F), while the expression of CALR (p = 0.019) in the DF group was lower than that in the control group (Figure 4A). CALR was also differentially expressed in the training set DF group; thus, this result was consistent with the expression difference in the validation group. These results suggest that CALR may play a key role in the pathogenesis of diabetic foot and be a potential biomarker.

FIGURE 4.

FIGURE 4

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Expression of the marker genes in the validation dataset. The expression of CALR (A) was reduced, while that of CD36 (B), NDRG1 (C), S100A7 (D), SIK2 (E) and TSC2 (F)was not significantly different in the treated group compared with the control group.

3.4. Functional analysis of the identified hub DE‐ARG, CALR

To better understand whether CALR can be used to distinguish between the normal and DF groups, a single‐gene GSEA was conducted. CALR was found to be mainly enriched in arginine and proline metabolism, ECM receptor interaction, focal adhesion, p53 signalling pathway, ribosome, small cell lung cancer, amoeboidal‐type cell migration, blood vessel morphogenesis, endothelial cell migration, regulation of cell adhesion, tissue migration and vasculature development (Figure 5A,B). Additionally, the human protein map database revealed that CALR was moderately enriched in endothelial cells and localized to the endoplasmic reticulum (Figure 5C,D). An interaction network of CALR and its protein was constructed, and the results were used to identify several closely related proteins, including APP, GABARAP, MTNR1A, SMARCB1, PDIA3, C11orf65, C1orf216, C6orf58, CNOT3, DNASE1L1, DRC7, EGFL8, EPCAM, EVA1B, FAHD2A, FUT2, GJA5, GNAO1, GSC2, HSD3B1, IDH1, ISY1, KLRC1, LGALS1, MAGEA2, MAGEA2B, MAVS, MMAB, MYBPHL, MYNN, NXPE1, PIAS4, POC1A, PPEF1, PRAM1, PSAT1, PSMC4, PSMD2, PYGO1, RAB31, RAB3C, RAP1B, RASSF2, RBM17, RBM5, SGTB, SLC25A11, SP6, SPATA17, SPRED2, TAF1B, TDO2, THAP7, THBS3, TPX2, TRNAU1AP, TUBB, USP12, ZHX1‐C8orf76, MBL2, HSPB1, TCTN2 and TAP1 (Figure 5E). Functional enrichment analysis of CALR and related genes using the DAVID database revealed their involvement in six signalling pathways including Epstein–Barr virus infection, phagosome, antigen processing and presentation, human immunodeficiency virus 1 infection, human cytomegalovirus infection and biosynthesis of cofactors (Figure 5F).

FIGURE 5.

FIGURE 5

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Functional analysis of CALR. (A, B) GSEA of CALR. (C, D)The expression of CALR in a single human cell type, subcellular localization of CALR and the interaction network of CALR and its related genes (E) in the HPA database. Functional enrichment of CALR and related genes through the DAVID database (F).

4. DISCUSSION

Diabetes is a growing public health concern and poses a significant burden on health services and economies worldwide. 19 , 20 , 21 Among the complications experienced by diabetic patients, diabetic foot is common. 22 This condition arises from the complex interaction between persistent hyperglycaemia and neuropathology, as well as disruptions in blood vessels and the immune system. 22 Mild cases may lead to foot deformities, dry skin and calluses, while severe cases can lead to foot ulcers and gangrene. 23 The primary objective of diabetic foot treatment is managing infections, facilitating wound healing, and preventing complications. This entails approaches such as wound cleansing, infection control, stress reduction, blood glucose regulation, nutritional support and surgical intervention. Additionally, patients should regularly seek medical foot examinations, learn how to self‐manage foot hygiene and footwear decisions, and prioritize foot protection. 21 , 24 Recent studies have demonstrated the significant role of diabetes‐induced angiogenesis disorders in the development of diabetes‐related diseases. Angiogenesis, a critical step in wound healing, is hindered by the reduction in angiogenesis caused by diabetes, particularly in individuals with diabetic foot. 25 , 26 Endothelial cells exposed to high glucose concentrations experience integrity loss and increased vulnerability to apoptosis, detachment and circulation in the bloodstream, which impairs angiogenesis. 27

While limited research exists on the relationship between DF and anoikis, mounting evidence suggests that anoikis could play a substantial role in the pathogenesis of this condition. Anoikis refers to a type of apoptosis triggered by the loss of adhesion to the extracellular matrix (ECM). 28 Normally, anoikis activation prevents exfoliated cells from attaching to unsuitable substrates, thereby safeguarding tissue development and stability and preventing abnormal cell growth and dysplasia. 28 , 29 , 30 In vitro experiments involving high glucose cultures, as well as in vivo and clinical studies on diabetes, have shown an increase in apoptosis among microvascular endothelial cells, suggesting the activation of programmed cell death mechanisms. 31 However, further research is needed to elucidate the precise molecular mechanisms of anoikis in vascular disorders related to diabetic foot.

To shed more light on the pathophysiology of diabetic foot, we conducted a preliminary investigation of anoikis‐related genes. We utilized the GSE7014, GSE80178 and GSE68183 dataset from the GEO database and downloaded anoikis‐related genes from GeneCard and Harmonizome. Subsequently, we identified 74 DEGs associated with anoikis. Functional enrichment analysis revealed that these DF‐related DEGs were mainly enriched in regulation of intrinsic apoptotic signalling pathway, anoikis, focal adhesion, cell−substrate junction, the PI3K‐Akt signalling pathway, the MAPK signalling pathway and apoptosis. Additionally, employing the LASSO algorithm and SVM‐RFE, we identified six DEGs related to diabetic foot and constructed an ROC curve. The analysis results gave an AUC value of 1.000, indicating excellent discriminatory capacity. 32 This suggests that the six identified DEGs have considerable accuracy and specificity in distinguishing DF samples from normal samples.

Finally, we validated the expression of the six hub genes using our own gene sequencing dataset. The results indicated no variation in the expression of SIK2, CD36, TSC2, NDRG1 and S100A7 between groups; meanwhile, CALR exhibited a consistent difference in expression. CALR is present in the endoplasmic reticulum, which is a key component in the quality control mechanism to ensure the correct folding of glycoproteins and contributes to calcium balance. 33 In addition, CALR plays a role in the cytoplasm outside the endoplasmic reticulum, cell surface and extracellular matrix, affecting a variety of processes, including proliferation, apoptosis, phagocytosis and the immune response. 34 Studies have confirmed that orosomucoid 1 (ORM1) promotes the progression of kidney renal clear cell carcinoma (KIRC) through CALR‐mediated apoptosis. 35

We conducted GSEA on CALR and found that this gene was mainly enriched in the focal adhesion, blood vessel morphogenesis, endothelial cell migration, regulation of cell adhesion, tissue migration and vasculature development. Furthermore, we examined the protein expression level of CALR in the Human Protein Atlas (HPA) database and found moderate expression in normal human endothelial cells, suggesting that changes in endothelial cells in diabetic foot may be associated with CALR. This can be confirmed through subsequent experiments. Sp et al. showed that AuNP‐calreticulin nanocomposites could promote the colony formation ability of endothelial cells in vitro and that diabetic mice treated with nanocomposites showed significantly faster wound healing in vivo. 36

To construct an interaction network of CALR and its associated proteins, we utilized the HPA database. The DAVID database was then utilized for KEGG enrichment analysis of related genes and subsequently visualized through a micro‐bioinformatics analysis website. This analysis revealed that CALR is significantly enriched in phagosome. The results of functional enrichment analysis of CALR‐related genes indicated that CALR may be involved in anoikis because induction of anoikis occurs when cells lose attachment to the ECM or adhere to an inappropriate type of ECM. Anoikis is a significant mechanism of cell death that plays a role in numerous physiological and pathological processes. 12 Humeau et al. mentioned in the study that CALR exposure is required for immunogenic cell death. 37 Studies have shown that calreticulin is important for the development of renal fibrosis and dysfunction in diabetic nephropathy. 38 The calreticulin on the cell surface interacts with LRP1 to exert its function as a heparin receptor, thereby preventing the response to high of intracellular glucose levels. This helps reprogramme cells after division, prompting them to synthesize extracellular hyaluronic acid matrix. 39 In summary, CALR may play a significant role in regulating anoikis signalling pathways related to diabetic foot. Additionally, CALR may impact diabetic foot through its effects on endothelial cell function and angiogenesis. Further research is warranted to better understand the specific role of CALR in diabetic foot pathogenesis and to identify new targets and strategies for its treatment and management.

Regarding the limitations of our research, importantly, our sample size was small. Thus, further verification with a larger sample set is needed. Additionally, although we validated the expression of CALR in DF and normal tissues using external datasets, we did not conduct in vivo and in vitro experiments for further confirmation. Future research should prioritize in vivo and in vitro experiments to elucidate the role and mechanism of CALR in the development of diabetic foot.

5. CONCLUSION

This study used comprehensive bioinformatics analysis to reveal the potential mechanism of association between anoikis genes and diabetic foot. CALR, an anoikis‐related gene, holds potential as a novel diagnostic and therapeutic target for diabetic foot. Nevertheless, additional molecular investigations are imperative to validate these discoveries.

FUNDING INFORMATION

This study was supported by the National Natural Science Foundation (82203760), Shandong Provincial Natural Science Foundation (ZR2020QH182, ZR2020MH158 and ZR2022MH155) and Qilu hygiene and health outstanding youth project.

CONFLICT OF INTEREST STATEMENT

The authors declare that there are no conflicts of interest.

ACKNOWLEDGEMENT

We thank American Journal Experts (https://www.aje.cn/about/) for its linguistic assistance during the preparation of this manuscript.

Su N, Wang J, Zhang H, et al. Identification and clinical validation of the role of anoikis‐related genes in diabetic foot. Int Wound J. 2024;21(3):e14771. doi: 10.1111/iwj.14771

Contributor Information

Xinyu Wang, Email: wangxinyu@sdu.edu.cn.

Ning Yang, Email: yangning@sdu.edu.cn.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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