Abstract

Purpose

   Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in
   cancer chemotherapy that greatly impacts the well-being of cancer
   patients. Currently, there is still an insufficiency of effective and
   reliable biomarkers in the field of clinical practice for the early
   detection of DIC. This study aimed to determine and validate the
   potential diagnostic and predictive values of critical signatures in
   DIC.

Methods

   We obtained high-throughput sequencing data from the GEO database and
   performed data analysis and visualization using R software, GO, KEGG
   and Cytoscape. Machine learning methods and weighted gene coexpression
   network (WGCNA) were used to identify key genes for diagnostic model
   construction. Receiver operating characteristic (ROC) analysis and a
   nomogram were used to assess their diagnostic values. A multiregulatory
   network was built to reveal the possible regulatory relationships of
   critical signatures. Cell-type identification by estimating relative
   subsets of RNA transcript (CIBERSORT) analysis was used to investigate
   differential immune cell infiltration. Additionally, a cell and animal
   model were constructed to investigate the relationship between the
   identified genes and DIC.

Results

   Among the 3713 differentially expressed genes, three key genes
   (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC
   curves based on three key genes showed excellent diagnostic predictive
   performance. The regulatory network analysis showed that the TFs CREB1,
   EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis
   of immune cell infiltration indicated that many immune cells (activated
   NK cells, M0 macrophages, activated dendritic cells and neutrophils)
   might be related to the progression of DIC. Furthermore, there may be
   various degrees of correlation between the three critical signatures
   and immune cells. RT‒qPCR demonstrated that the mRNA expression of
   CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was
   significantly downregulated in DOX-treated cardiomyocytes in vitro and
   in vivo.

Conclusion

   Our study suggested that the differential expression of CSGALNACT1,
   ZNF296 and FANCB is associated with cardiotoxicity and is also involved
   in immune cell infiltration in DIC. They might be potential biomarkers
   for the early occurrence of DIC.

   Keywords: doxorubicin, cardiotoxicity, biomarker, machine learning,
   immune infiltration

Introduction

   With the significant advancement in early detection and treatment, the
   number of cancer survivors has increased,[46]^1 but there have also
   been multiple adverse outcomes, such as chemotherapy-induced
   cardiotoxicity.[47]^2 Doxorubicin (DOX), a member of the anthracycline
   antibiotic family, is extensively used in the chemotherapy of various
   cancers, such as hematological malignancies and other tumors.[48]^3
   Unfortunately, approximately a quarter of cancer survivors experience
   DOX-induced cardiotoxicity (DIC), which significantly limits its
   clinical application.[49]^4 Although DIC has attracted the attention of
   cardiologists, there are limited methods available for early detection
   and treatment in clinical practice.

   The pathophysiological mechanisms of DIC are complex and not yet fully
   elucidated, including oxidative stress, mitochondrial dysfunction,
   inflammatory response, metabolic disorders, and cell death.[50]^5–8 An
   increasing amount of evidence has indicated that immune cells have
   crucial functions in cardiac homeostasis and disease.[51]^9^,[52]^10
   Further investigation is required to explore the precise function and
   molecular mechanism of immune cells in the development of DIC. The
   rapid developments of multiomics and high-throughput sequencing provide
   novel tools for identifying early biomarkers and exploring therapeutic
   targets.[53]^11

   Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs)
   has the ability of unlimited proliferation and differentiation into
   cardiomyocytes, so it is widely used in the study and treatment of
   cardiac disease.[54]^12^,[55]^13 Recently, Burridge et al indicated
   that the viability of using hiPSC-CMs as a platform for determining and
   validating the genetic foundation and molecular mechanisms underlying
   DIC.[56]^14 In addition, many studies have also used hiPSC-CMs as the
   cell source of DIC.[57]^15–17 On these bases, we downloaded
   high-throughput gene expression data from NCBI GEO database.

   In the current work, we explored key genes of DIC using comprehensive
   bioinformatics approaches and machine-learning strategies. Furthermore,
   we constructed a multiregulatory network to uncover the possible
   regulatory connections of key genes. Similarly, machine learning was
   carried out to construct a diagnostic nomogram model for DIC prediction
   on the basis of the three key genes (ZNF296, FANCB and CSGALNACT1) that
   were discovered in DIC pathogenic genes. Therefore, the study of its
   related molecular mechanism may be helpful for its further clinical
   application in DIC. We attempted to explore the connection between key
   genes and immunity. Finally, our study validated the differential
   expression of the key genes by constructing the model treated with
   doxorubicin in vitro and in vivo. This study collectively identified a
   stable and achievable biomarker in DIC, which could potentially serve
   as a target for diagnosing and preventing cancer survivors who have
   undergone DOX treatment.

Materials and Methods

Data Acquisition and Homogenization

   Our information was gathered by accessing the Gene Expression Omnibus
   (GEO) database ([58]https://www.ncbi.nlm.nih.gov/geo/), which is a
   public repository of high-throughput gene expression data, chips, and
   microarrays.[59]^18 High-throughput gene expression data were
   downloaded from the NCBI GEO database, including [60]GSE76314,
   [61]GSE157282 and [62]GSE181517. [63]GPL11154 served as the
   complementary high-throughput detection platform for [64]GSE76314;
   [65]GSE157282 was based on platform [66]GPL24676. [67]GSE181517 was
   based on platform [68]GPL18573. The [69]GSE76314 dataset,[70]^14 which
   describes the differences in cell-derived cardiomyocytes (hiPSC-CMs),
   was used as the training set (3 doxorubicin samples vs 3 control
   samples). The [71]GSE157282 dataset[72]^19 [doxorubicin samples vs 3
   controls samples], which describes the differences in the hiPSC-CMs,
   was also used as the training set. [73]GSE181517[74]^20 was used as the
   validation set to explore the dynamic changes in the critical
   signature, which included 3 doxorubicin samples and 3 control samples.
   The scale function in R version 4.2.0 software was used to combine
   [75]GSE76314 and [76]GSE157282 into one dataset and calibrate the batch
   using the SVA package.[77]^21 For uniform manifold approximation and
   projection (UMAP), after the removal of the batch effect, the samples
   among various datasets are clustered and intertwined with each other,
   indicating that the batch effect is better removed. Principal component
   analysis (PCA) was used to verify the reproducibility of the data, and
   the R package ggord was used to construct the PCA plots.[78]^22

Analysis of Differentially Expressed Genes

   In this study, we performed differential expression analysis using the
   R Bioconductor package “Limma” (V3.46.0) to identify differentially
   expressed genes.[79]^23 Differentially expressed genes (DEGs) were
   defined as those with adjusted P values and genes with adjusted P
   values < 0.05 and |Log2-fold-change (log2FC)|≥ 1 after adjusting the P
   values using Benjamini‒Hochberg’s false discovery rate (FDR). The R
   software ggplot2 V3.3.5 package was used to create volcano plots, and
   the R software Heatmap V1.0.12 package was utilized to generate
   heatmaps for the top 100 DEGs from each dataset.

Functional Enrichment Analysis of DEGs

   A functional enrichment analysis of DEGs was performed to clarify their
   role using the R software clusterProfiler and Goplot package. These
   analyses were discerningly marked by significance, indicated by the
   adjusted P value < 0.05. A comprehensive enrichment analysis of Kyoto
   Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) was
   conducted and visualized, including biological process (BP), cellular
   component (CC), and molecular function (MF) for all DEGs.

Screening and Validation of Critical Gene Signatures

   In this study, random forests (RF),[80]^24 least absolute shrinkage and
   selection operator (LASSO) logistic regression,[81]^25 and weighted
   gene coexpression network analysis (WGCNA)[82]^26 were utilized to
   screen novel and pivotal signatures associated with DIC. The RF model,
   LASSO analysis, and WGCNA were executed using the R software random
   Forest package, R software glmnet package and R software WGCNA package,
   respectively. Following the individual algorithmic analyses, the points
   of intersection among the three classification models were identified,
   setting the stage for the next step of the analysis. To assess the
   diagnostic efficacy of these three diagnostic signatures in
   doxorubicin-induced cardiotoxicity, the area under the curve (AUC) and
   the ROC curves were established using R software’s pROC package. In
   adherence to statistical standards, a threshold of two-sided P < 0.05
   was deemed to be statistically significant. To verify the expression
   levels of key markers, their differences between the doxorubicin and
   control groups were assessed in the training and validation sets.

Multifactor Regulatory Network Analysis of Key Genes

   Guardians of gene expression include long noncoding RNAs (lncRNAs),
   microRNAs (miRNAs), and transcription factors (TFs) in transcription
   and posttranscription prototypes. To demonstrate the regulatory
   networks of key signatures, lncRNAs, mRNAs, TFs, and miRNAs were
   predicted by RNAInter,[83]^27 hTFtarget,[84]^28 and mirDIP.[85]^29
   Then, we used Cytoscape software[86]^30 to visualize the multifactor
   regulatory network.

Analysis of Immune Cell Infiltration on Critical Gene Signatures

   Prior to CIBERSORT analysis, principal component analysis (PCA) of the
   gene expression profile was plotted and visualized by the R software
   factoextra package. Analysis of 22 different types of immune cells
   using CIBERSORT demonstrated the differential expression of cells
   between the doxorubicin group and the control group using a box diagram
   (P value < 0.05 was deemed to be statistically significant).
   Afterwards, Spearman correlation analysis between immune cells and
   immune cells, as well as between key signatures and immune cells, was
   demonstrated by a heatmap through the R software corrplot V0.92
   package.

Cell Line Culture and Treatment

   AC16 immortalized human cardiomyocyte line was procured from MeisenCTCC
   (Zhejiang, China) and cultured in Dulbecco’s modified Eagle’s medium
   (DMEM) containing 10% fetal bovine serum (FBS) 1% and
   penicillin/streptomycin and grown in a CO[2] incubator maintained at
   atmospheric oxygen levels and 5% CO[2]. When the AC16 cell density
   reached 80–90%, the cells were exposed to 1 µM doxorubicin
   (Sigma‒Aldrich) or vehicle (PBS). Whole cells were collected after 24 h
   of treatment.

Animal Experiments

The Establishment of Animal Model

   Twenty male wild-type C57BL/6 mice aged 8 weeks (18–22g) were purchased
   from the SiPeiFu (Beijing) Biotechnology Co., Ltd., Beijing City, and
   they were randomly divided into two groups: normal control (NC) group
   and DOX treatment (DOX) group. In NC group, mice were i.p.injected with
   the equivalent volume of saline two times (on days 1 and 8,
   respectively); in DOX group, mice were intraperitoneally injected with
   DOX on day 1 (10 mg/kg) and day 8 (10 mg/kg), and acute DIC model was
   established in vivo (cumulative dose of 20 mg/kg).[87]^31 At the end of
   the experiment, each group of mice was anesthetized and killed, and
   their hearts were taken for subsequent experiments. The experimental
   procedures were implemented in accordance with the China Animal Welfare
   Legislation, reviewed and approved by the Qingdao University Committee
   on Ethics in the Care and Use of Laboratory.

Cardiac Functions Examination

   At the end of the treatment, the mice heart function was examined
   through echocardiography Imaging System (VINNO 6 LAB, VINNO). Cardiac
   ventricular structure and systolic function were examined by
   two-dimensional imaging motion mode to record parasternal long-axis
   fields. Parameters included the left ventricular (LV) anterior wall
   thickness in the end-diastolic/end- systolic stages, the LV posterior
   wall thickness, the LV internal diameter; LV ejection fraction, and LV
   fractional shortening.

The Hematoxylin & Eosin Staining of Heart Tissues

   Hearts of mice were fixed in 10% formalin, and then treated with
   dehydration, embedding and sectioning. Hematoxylin & eosin staining was
   performed to examine cardiac histological morphology.

RNA Extraction and Quantitative Real-Time PCR for Cell and Animal
Verification Experiments

   The TRIzol method was used to extract total RNA from AC16 cells and
   heart tissue samples, and spectrophotometry was used to measure the
   concentration and purity. According to the manufacturer’s instructions,
   equivalent amounts (1 μg) of purified RNA were used as a template to
   synthesize cDNA using ABScrlpt III RT Master Mix for qPCR with a gDNA
   Remover Kit (Cat# RK20429, ABclonal, Wuhan, China). The manufacturer’s
   instructions were followed to perform quantitative PCR using a
   QuantStudio 3 (Bio-Rad) and 2X Universal SYBR Green Fast qPCR Mix
   (Cat#RK21203, ABclonal, Wuhan, China). The relative expression levels
   were determined by the 2^–ΔΔCT method and are presented relative to
   GAPDH mRNA. Primer sequences are provided in [88]Table S1.

Statistical Analysis

   GraphPad Prism 9.0 was utilized for conducting the statistical
   analysis. The unpaired Student’s t-test was used to compare the DOX
   group (DOX) and control group (NC). The assays were conducted at least
   three times or more. A two-tailed p value <0.05 was considered
   statistically significant.

Results

Identification of DEGs

   [89]Figure 1 clarifies the depicted flow chart of the present work.
   Both the [90]GSE76314 and [91]GSE157282 datasets were merged
   ([92]Figure 2A) for the following analysis. The utilization of UMAP was
   performed to visually represent the clustering of read counts and
   identify batch effects ([93]Figure 2B). Next, we merged these two
   datasets into one dataset and rectified the batch using the SVA
   software package. [94]Figure 2C shows that the interbatch variations
   are effectively removed after data normalization. Finally, a normalized
   gene expression matrix file containing 12 samples (6 doxorubicin
   samples and 6 control samples) was obtained. These DEGs are distributed
   in the volcano map ([95]Figure 2D). The first 100 temperatures are
   shown in the heatmap ([96]Figure 2E). In the results of differential
   expression analysis, a total of 3713 genes were identified as DEGs,
   including 1925 upregulated genes and 1788 downregulated genes
   ([97]Figure 2F).

Figure 1.

   [98]Figure 1
   [99]Open in a new tab

   Flow chart of this study.

Figure 2.

   [100]Figure 2
   [101]Open in a new tab

   Identification of differentially expressed genes (DEGs). (A) The
   intersection of genes between [102]GSE76314 and [103]GSE157282. (B)
   UMAP before the batch correction of two datasets. (C) UMAP after the
   batch correction of two datasets. (D) Volcano plots of DEGs. Nodes in
   red represent upregulated genes, nodes in green represent downregulated
   genes, and black dots represent no significantly changed genes. (E)
   Heatmaps of DEGs. The legend on the top right indicates the log fold
   change of the genes. The horizontal axis represents each sample, and
   the vertical axis represents each gene. Blue and red colors represent
   low and high expression values, respectively. (F) Number of DEGs.

GO and KEGG Pathway Analysis of DEGs

   Subsequently, we performed enrichment analyses for overlapping DEGs in
   KEGG and GO. Among the KEGG pathways, the pathways in cancer, erpes
   simplex virus 1 infection, rap1 signaling pathway, alcoholism, and
   cGMP-PKG signaling pathway were considered the pathways with the
   highest degree of enriched pathways ([104]Figure 3A). In significantly
   enriched BP, these included the positive regulation of nucleobase
   containing compound metabolic process, positive regulation of
   biosynthetic process, cell population proliferation, cell cycle, and
   apoptotic process ([105]Figure 3B). Subsequently, chromosome,
   chromatin, microtubule cytoskeleton, transcription regulator complex,
   and chromosomal region were the most enriched terms in CC ([106]Figure
   3C). For MF, the top 5 enriched items were transcription regulator
   activity, sequence-specific DNA binding, ribonucleotide binding, DNA
   binding transcription factor activity, and adenyl nucleotide binding
   ([107]Figure 3D).

Figure 3.

   [108]Figure 3
   [109]Open in a new tab

   Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway enrichment analysis of differentially expressed genes (DEGs).
   (A) Bubble chart shows KEGG-enriched items of DEGs. (B–D) Bubble charts
   show GO-enriched items of DEGs in three functional groups: biological
   processes (BP, B), cell composition (CC, C), and molecular function
   (MF, D). The x-axis labels represent gene ratios, and the y-axis labels
   represent GO terms. The size of the circle represents the gene count.
   Different colors of circles represent different adjusted P values.

Screening of Critical Signatures

   Here, three key genes were screened using the RF, LASSO, and WGCNA
   machine learning algorithms. Using the WGCNA algorithm ([110]Figure
   S1), we confirmed 11 extraordinary coexpression modules ([111]Figure
   4A). [112]Figure 4B clarifies that there is a correlation between
   multiple modules in DIC. Given that the turquoise and dark red modules
   are most significantly associated with DIC, we screened genes in the
   turquoise and dark red modules for follow-up exploration and
   successfully identified 3370 genes ([113]Figure 4C–E, [114]Table S2).
   Moreover, the results showed that 15 genes were identified by the LASSO
   algorithm ([115]Figure 4F and [116]G, [117]Table S2), and 90 genes were
   determined with the RF algorithm ([118]Figure 4H–J, [119]Table S2).
   Subsequently, a Venn diagram shows that FANCB, ZNF296 and CSGALNACT1
   obtained by the three algorithms are overlapping genes ([120]Figure
   4K).

Figure 4.

   [121]Figure 4
   [122]Open in a new tab

   Screening of critical signatures via multiple machine-learning
   algorithms. (A) Clustering dendrogram of differentially expressed genes
   related to DIC, with dissimilarity based on topological overlap,
   together with assigned module colors. (B) Module feature vector
   clustering. (C) Module–trait associations. The gene significance for
   doxorubicin cardiotoxicity in the turquoise and dark red modules, and
   one dot represents one gene in the turquoise (D) and dark red modules
   (E). (F) LASSO coefficient profile of the 12 genes, and different
   colors represent different genes. (G) Selection of the optimal
   parameter (lambda) in the LASSO model and generation of a coefficient
   profile plot. (H) Variable importance, as measured by the mean decrease
   in accuracy (left panel) or the Gini coefficient (right panel), is
   computed using the OOB error. Genes are shown in descending order of
   importance. Distribution of the out-of-band (OOB) error rate at various
   values of mtry (I) and trees (J). (K) Venn diagram showing the
   intersection of critical signatures obtained by the three strategies.

Verification of FANCB, ZNF296 and CSGALNACT1 in DIC

   To evaluate the possible significance of the key genes in DIC, we
   created ROC curves. First, we verified the expression of FANCB, ZNF296
   and CSGALNACT1 in DIC. The results showed that compared with the
   control group, the expression of ZNF296 and CSGALNACT1 in the training
   and validation sets was significantly upregulated, and the expression
   of FANCB was significantly downregulated (all P < 0.01, [123]Figure 5A
   and [124]B). Second, the AUCs of the three key genes in the training
   set were 1 ([125]Figure 5C), indicating that they were accurate and
   reliable in terms of predictive value. In the validation set of
   [126]GSE181517, the AUCs for FANCB, ZNF296 and CSGALNACT1 were also all
   1 ([127]Figure 5D). Finally, we created nomogram models to diagnose DIC
   using the hallmark genes FANCB, ZNF296 and CSGALNACT1 ([128]Figure 5E).

Figure 5.

   [129]Figure 5
   [130]Open in a new tab

   The diagnostic power of FANCB, ZNF296 and CSGALNACT1 in DIC by ROC
   curve. (A) The expression of FANCB, ZNF296 and CSGALNACT1 in the
   training set. (B) The expression of FANCB, ZNF296 and CSGALNACT1 in the
   validation set. (C) The ROC curve of FANCB, ZNF296 and CSGALNACT1 in
   the training set. (D) The ROC curve of FANCB, ZNF296 and CSGALNACT1 in
   the validation set. (E) A nomogram was used to predict the occurrence
   of DIC. (*P<0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, - P ≥
   0.05).

Construction of a Multifactor Regulatory Network Based on Key Genes

   Next, we explored the potential regulatory mechanisms by studying the
   regulatory networks involving lncRNAs, miRNAs and TFs and conducted a
   comprehensive visual analysis ([131]Figure 6). Here, to construct a
   multifactor regulatory network, interaction pairs involving miRNAs,
   lncRNAs and TFs with three key genes were extracted. In the multifactor
   regulatory network, FANCB was managed by 5 miRNAs and 1 lncRNA,
   including hsa-miR-4695-5p, hsa-miR-3612, hsa-miR-2113, hsa-miR-7109-3p,
   hsa-miR-135a-2-3p, and CISTR, and the possible TFs for FANCB were ATF3,
   CEBPA, CEBPB, CREB1, E2F1, EP300, ETS1, FLI1, FOXA1, GABPA, HDAC1,
   HDAC2, KLF5, MAX, and MAZ. Additionally, the ZNF296 network included 2
   lncRNAs, 7 miRNAs, and 66 TFs, and the CSGALNACT1 network contained 167
   miRNAs and 25 TFs. Obviously, the results of our work suggest that the
   TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulate all 3 marker
   genes.

Figure 6.

   [132]Figure 6
   [133]Open in a new tab

   The multifactor regulatory network based on FANCB, ZNF296 and
   CSGALNACT1. FANCB, FA complementation group B; ZNF296, zinc finger
   protein 296; CSGALNACT1, chondroitin sulfate
   N-acetylgalactosaminyltransferase 1.

Analysis of Immune Cell Infiltration and Correlation Analysis Between Key
Genes

   Recent evidence has shown that immune cells play a critical role in the
   pathophysiological process of DIC.[134]^6^,[135]^32^,[136]^33
   Therefore, we investigated the link between key features in DIC and
   immune infiltration. The swatches of different groups are well divided
   in the training set in PCA ([137]Figure 7A). To study 22 types of
   immune cell phenotypes, we utilized the CIBERSORT algorithm to analyze
   the training set. Compared with the control samples, DIC samples
   exhibited a greater percentage of activated NK cells, M0 macrophages,
   activated dendritic cells and neutrophils (all P < 0.05). However, the
   proportions of memory B cells, CD8 T cells, resting memory CD4 T cells,
   and resting NK cells in DIC were relatively lower than those in the
   control (all P < 0.05) ([138]Figure 7B). Twenty immune cell types were
   included for further investigation after two immune cell types with
   undetectable levels were excluded. It can be seen from the related
   heatmaps ([139]Figure 7C) that naive B cells and CD8 T cells, naive B
   cells and resting memory CD4 T cells, CD8 T cells and neutrophils,
   resting NK cells and activated M0 macrophages displayed the most
   significant negative correlations (P < 0.0001). Naive B cells and
   neutrophils, activated mast cells and monocytes, and M0 macrophages and
   neutrophils exhibited the most significant positive correlations (P <
   0.0001). Our aim was to detect the link between key genes and 20 types
   of immune cell phenotypes in doxorubicin cardiotoxicity. According to
   the correlation analysis results, FANCB displayed the most positive
   correlations with resting NK cells and CD8 T cells [r = 0.70, -log10 (p
   value) = 1.94] while demonstrating negative correlations with activated
   dendritic cells [r = −0.76, -log10 (p value) = 2.35]. ZNF296 displayed
   the most positive correlations with activated dendritic cells [r =
   0.76, -log10 (p value) = 2.40] and revealed negative correlations with
   resting NK cells [r = −0.80, -log10 (p value) =2.72]. CSGALNACT1
   displayed the most positive correlations with M0 macrophages [r = 0.92,
   -log10(p value) = 4.60] and revealed negative correlations with resting
   NK cells [r = −0.79, -log10(p value) =2.65] ([140]Figure 7D).

Figure 7.

   [141]Figure 7
   [142]Open in a new tab

   Immune cell infiltration analysis and relationships between critical
   signatures and immune cells in DIC. (A) Principal component analysis
   (PCA) cluster plot of gene expression profiles between doxorubicin
   samples and control samples in the training set. (B) Box plot of the
   proportion of 22 types of immune cells. (C) Heatmap of correlations in
   20 types of immune cells. The colored squares represent the strength of
   the correlation; red represents a positive correlation, and blue
   represents a negative correlation. Darker color implies stronger
   association. (D) Correlations between FANCB, ZNF296, CSGALNACT1, and
   infiltrating immune cells. (*P<0.05, **P < 0.01, ***P < 0.001, ****P <
   0.0001, - P ≥ 0.05).

Verification of FANCB, ZNF296 and CSGALNACT1 in AC16 Human Cardiomyocyte-Like
Cells

   To verify whether the mRNA expression of FANCB, ZNF296 and CSGALNACT1
   was consistent with the results of the database analysis, we
   constructed a doxorubicin-induced injury model in vitro.[143]^34 The
   results showed that FANCB was expressed at a low level ([144]Figure
   8A), while ZNF296 and CSGALNACT1 were expressed at a high level in the
   DOX group ([145]Figure 8B and [146]C).

Figure 8.

   [147]Figure 8
   [148]Open in a new tab

   The mRNA expression of FANCB (A), ZNF296 (B) and CSGALNACT1 (C) in AC16
   human cardiomyocyte cells by RT‒qPCR. (**P < 0.01, ***P < 0.001).

The Verification Experiments in vivo

   Firstly, mouse model of DIC is illustrated in [149]Figure 9A, and the
   heart weight to tibia length ratio (HW/TL) was significantly reduced in
   the DOX group ([150]Figure 9B). The echocardiography results
   demonstrate a downward trend of cardiac systolic and diastolic function
   in the DOX group ([151]Figure 9C), as evidenced by significantly
   decreased ejection fraction ([152]Figure 9D), decreased fractional
   shortening ([153]Figure 9E). HE staining illustrated that DOX treatment
   exacerbated cardiomyocyte tissue disorders compared with control group
   ([154]Figure 9F). These results indicate that we successfully
   established a DIC model. Finally, the mRNA levels of FANCB was
   decreased in the hearts of DOX treated group relative to the control
   group ([155]Figure 9G), while ZNF296 and CSGALNACT1 is contrary to the
   FANCB ([156]Figure 9H and [157]I).

Figure 9.

   [158]Figure 9
   [159]Open in a new tab

   The mRNA expression of FANCB, ZNF296, and CSGALNACT1 in DOX-induced
   cardiotoxicity in vivo. (A) Male C57BL/6 mice were i.p.injected with an
   accumulation does of 20 mg/kg DOX or the equivalent volume of saline
   two times (on Days 1 and 8, respectively). (B) the HW/TL ratio was
   measured. (C) The mice were tested for cardiac function by
   echocardiography. (D and E) The LV trace EF% and LV trace FS% were
   calculated. (F) HE staining of heart tissue in Control group and DOX
   group. (G) FANCB mRNA expression was detected by RT-qPCR, (H–I) ZNF296,
   and CSGALNACT1 mRNA expression was detected by RT-qPCR. (*P<0.05, **P <
   0.01, ****P < 0.0001).

Discussion

   Doxorubicin prolongs the survival time of tumor patients, but it also
   leads to severe side effects, especially cardiotoxicity, which
   significantly affects the effectiveness of anticancer
   treatment.[160]^35^,[161]^36 Hence, it is crucial to recognize secure
   and effective biomarkers to detect DIC. Currently, with the rapid
   development of high-throughput sequencing and multiomics
   technologies,[162]^37^,[163]^38 some new methods have been used to
   detect and diagnose DIC.[164]^11

   In this study, 3713 DEGs were discovered using a comprehensive
   bioinformatics analysis between DIC and the controls. The results
   indicated that the significantly enriched terms included positive
   regulation of nucleobase-containing compound metabolic process in BP,
   chromosome in CC, and transcription regulator activity in MF. Of note,
   multiple algorithms were applied to identify key genes in DIC,
   including WGCNA, RF and LASSO. Ultimately, CSGALNACT1, ZNF296 and FANCB
   were determined to be key genes. Additionally, through ROC curves and
   nomogram analysis, it was shown that the expression levels of
   CSGALNACT1, ZNF296 and FANCB could accurately distinguish DIC from
   controls. Similarly, we validated the stability of CSGALNACT1, ZNF296
   and FANCB expression levels through distinct validation datasets. From
   the results, CSGALNACT1, ZNF296 and FANCB may be potential factors for
   DIC, which suggests that they have vital value for clinical application
   in DIC. Additionally, our regulatory network showed that the TFs CREB1,
   EP300, FLI1, FOXA1, MAX, and MAZ modulated all 3 marker genes. However,
   the transcriptional regulation of CSGALNACT1, ZNF296 and FANCB by
   CREB1, EP300, FLI1, FOXA1, MAX and MAZ needs further study.

   Zinc finger protein 296 (ZNF296 or Zfp296) is a reprogramming factor
   composed of 445 amino acids that belongs to the C2H2 zinc finger
   protein family and is involved in cell proliferation, survival,
   differentiation, and carcinogenesis.[165]^39 ZNF296 is expressed in
   mouse testes, bone marrow, and embryos.[166]^40 Previous evidence
   suggests that ZNF296 is an effective biomarker for breast cancer
   detection.[167]^41 Furthermore, recent studies have found that ZNF296
   is a regulator of H3K9me3 and is closely associated with the
   pluripotency of embryonic stem cells.[168]^42 Another study indicated
   that ZNF296 was associated with the formation of induced pluripotent
   stem (iPS) cells.[169]^43 Interestingly, a microarray analysis based on
   transcriptomic signatures showed that ZNF296 was upregulated in
   tuberculosis and could be used as a diagnostic biomarker.[170]^44 In
   the present study, we also found that ZNF296 was significantly
   upregulated in the DOX-treated group, which was validated through
   platforms and across cell experiments. Although the role of ZNF296 in
   DIC has not been clearly discussed, it may be a potential diagnostic
   biomarker.

   Furthermore, CSGALNCT-1, also named chondroitin sulfate
   N-acetylgalactosaminyltransferase 1, plays a vital role as a
   glycosyltransferase in the biosynthesis of chondroitin sulfate. It is
   also involved in important biological processes, such as cartilage
   development and polysaccharide metabolism.[171]^45 Previous microarray
   studies have indicated that CSGALNACT1 is a prognostic biomarker in
   multiple myeloma.[172]^46 According to a recent study, CSGALNACT1 was
   found to have a notable upregulation in lung-derived mesenchymal stem
   cells (LMSCs) from emphysema patients, which can regulate extracellular
   matrix (ECM), but the specific function remains unclear.[173]^47
   Significantly, chondroitin sulfate (CS) is a natural macromolecule
   catalyzed by CSGALNACT1 and a major component of the extracellular
   matrix.[174]^48 Another study published recently showed that abnormal
   accumulation of chondroitin sulfate glycosaminoglycans led to cardiac
   fibrosis.[175]^49 Interestingly, our study showed that CSGALNACT1 was
   expressively upregulated in cardiomyocytes in the doxorubicin-treated
   group. Therefore, we speculate that CSGALNACT1 may be involved in DIC,
   although the specific mechanisms require further investigation. In
   conclusion, CSGALNACT1 is considered a suitable biomarker for DIC.

   FANCB, fully defined as Fanconi anemia complementation group B and
   known as FAAP95, is an essential constituent of the Fanconi anemia (FA)
   core complex. It has a vital function in hematopoiesis and the
   development of germ cells.[176]^50^,[177]^51 Some studies have
   indicated that the FA pathway serves as an indispensable mechanism for
   the DNA damage response.[178]^52 Further study has shown that FANCB is
   known as a DNA repair gene.[179]^53 Previous studies have concluded
   that DNA damage is one of the molecular mechanisms in DIC.
   Interestingly, our study found that the expression of FANCB in
   cardiomyocytes treated with doxorubicin was significantly reduced,
   which was consistent with the verification of the cell experiment.
   Therefore, we speculate that FANCB is directly or indirectly involved
   in DIC via DNA damage/repair. Although the exact mechanism remains
   unclear, this study provides a novel perspective for understanding
   doxorubicin-induced cardiomyocyte injury.

   Noncoding RNAs (ncRNAs) widely participate in cell physiological and
   pathological processes by regulating the epigenetic modification of
   gene expression, which has attracted great attention.[180]^54^,[181]^55
   Increasing evidence has reported the significant role of ncRNA in the
   DIC.[182]^56 Therefore, we further predicted upstream ncRNAs targeting
   three key genes, ZNF296, FANCB, and CSGALNACT1, and ultimately
   constructed a gene regulatory network including microRNAs and lncRNAs.
   Additionally, we also investigated the regulatory network between
   transcription factors and three core genes. Notably, the transcription
   factors CREB1, EP300, FLI1, FOXA1, MAX, and MAZ were simultaneously
   regulated by three key genes.

   In recent years, a growing body of research has highlighted the
   potential impact of immune cells on DIC, including
   neutrophils,[183]^57^,[184]^58 macrophages,[185]^59^,[186]^60 and NK
   cells.[187]^61 In our study, significant differences were observed
   between the DOX-treated group and the control group in terms of immune
   cell infiltration. Specifically, the peaks of M0 macrophages, activated
   NK cells, activated dendritic cells, and neutrophils were higher, while
   the proportions of memory B cells, CD8 T cells, resting CD4 T cells,
   and resting NK cells were lower. Previous studies have shown that 24
   hours after doxorubicin treatment, neutrophils significantly infiltrate
   the heart, accompanied by a decrease in cardiac function, vascular
   structure damage, and increased deposition of vascular collagen,
   leading to fibrosis,[188]^58 which is consistent with our study
   findings. Notably, macrophages, a type of innate immune system cell,
   were also found to be upregulated in our study. Some studies found that
   in an acute doxorubicin-induced cardiotoxicity mouse model, doxorubicin
   increased the proportion of M1 macrophages while suppressing M2
   macrophages.[189]^62 A macrophage profile tracking study based on
   CX3CR1 found that M1 macrophages may be the major group during the
   early stage of cardiac injury.[190]^63 Although studies have suggested
   that immune cells play an important role in DIC, the specific
   regulatory mechanisms remain unclear.

   Therefore, we attempted to reveal the potential function of immune
   cells by establishing a connection between key genes and immune cells.
   In our research, CSGALNACT1, ZNF296 and FANCB showed different degrees
   of correlation with immune cells, including resting NK cells and
   activated dendritic cells. In a prior study, it was demonstrated that
   CSGALNACT1 was linked to the activation of lymphocytes and the
   production of inflammatory cytokines (IL-6 and IFN-r cytokine) in
   experimental autoimmune encephalomyelitis (EAE).[191]^64 In our data,
   CSGALNACT1 displayed the most positive correlations with M0 macrophages
   and showed negative correlations with resting NK cells. Our findings
   provide novel insights into the immune mechanisms of CSGALNACT1,
   ZNF296, and FANCB during DIC.

   There are some limitations in our study. First, although we have
   integrated multiple datasets, the sample size was still relatively
   small. Second, due to the lack of relevant clinical data, we did not
   perform prognostic analysis. Third, we validated the mRNA expression of
   critical key genes in DOX-treated cardiomyocytes, but further
   validation, such as Western blotting and immunofluorescence staining,
   is necessary. To improve these deficiencies, we will conduct further
   clinical studies to confirm the findings with more detailed clinical
   information and a wide range of samples.

Conclusion

   In summary, we concluded that (1) three key genes, CSGALNACT1, ZNF296,
   and FANCB, were identified that might be probable diagnostic biomarkers
   in DIC and may contribute to immunity regulation simultaneously; (2)
   immune cell infiltration was closely related to DIC; and (3) the
   inflammatory response signaling pathway, biosynthetic process, and
   apoptotic process may be the key mechanisms in DIC. This study provides
   a new strategy for screening and evaluating biomarkers in clinical
   practice while also laying the foundation for the underlying role of
   immune cell responses in DIC.

Acknowledgments