Abstract

   The relationship between non-alcoholic fatty liver disease (NAFLD) and
   triple-negative breast cancer (TNBC) has been widely recognized, but
   the underlying mechanisms are still unknown. The objective of this
   study was to identify the hub genes associated with NAFLD and TNBC, and
   to explore the potential co-pathogenesis and prognostic linkage of
   these two diseases. We used GEO, TCGA, STRING, ssGSEA, and Rstudio to
   investigate the common differentially expressed genes (DEGs), conduct
   functional and signaling pathway enrichment analyses, and determine
   prognostic value between TNBC and NAFLD. GO and KEGG enrichment
   analyses of the common DEGs showed that they were enriched in leukocyte
   aggregation, migration and adhesion, apoptosis regulation, and the PPAR
   signaling pathway. Fourteen candidate hub genes most likely to mediate
   NAFLD and TNBC occurrence were identified and validation results in a
   new cohort showed that ITGB2, RAC2, ITGAM, and CYBA were upregulated in
   both diseases. A univariate Cox analysis suggested that high expression
   levels of ITGB2, RAC2, ITGAM, and CXCL10 were associated with a good
   prognosis in TNBC. Immune infiltration analysis of TNBC samples showed
   that NCF2, ICAM1, and CXCL10 were significantly associated with
   activated CD8 T cells and activated CD4 T cells. NCF2, CXCL10, and CYBB
   were correlated with regulatory T cells and myeloid-derived suppressor
   cells. This study demonstrated that the redox reactions regulated by
   the NADPH oxidase (NOX) subunit genes and the transport and activation
   of immune cells regulated by integrins may play a central role in the
   co-occurrence trend of NAFLD and TNBC. Additionally, ITGB2, RAC2, and
   ITGAM were upregulated in both diseases and were prognostic protective
   factors of TNBC; they may be potential therapeutic targets for
   treatment of TNBC patients with NAFLD, but further experimental studies
   are still needed.

   Keywords: triple-negative breast cancer (TNBC), non-alcoholic fatty
   liver disease (NAFLD), non-alcoholic steatohepatitis (NASH),
   bioinformatics analysis, hub genes, prognostic value

1. Introduction

   Breast cancer is the most commonly diagnosed cancer and the leading
   cause of cancer death among women [[32]1]. Triple-negative breast
   cancer (TNBC), defined by estrogen receptor (ER)-negative, progesterone
   receptor (PR)-negative, and human epidermal growth factor receptor-2
   (HER2)-negative histological presentation, accounts for approximately
   20% of all breast cancer cases [[33]2,[34]3]. In contrast to other
   breast cancer types, TNBC has a more aggressive expression profile
   (high p53 and Ki67 and low Bcl-2 expression), large tumor size, and
   high histological grade, and is associated with an increased risk of
   early relapse and poor prognosis [[35]4]. Many potential risk factors
   for TNBC have been reported, including non-modifiable factors such as
   age, sex, race, genetic mutations, breast tissue density, and family
   history of breast disease, and modifiable factors such as diet,
   lifestyle, obesity, and hormone replacement therapy
   [[36]5,[37]6,[38]7]. In particular, the increasing proportion of
   obesity worldwide has led to a sharp rise in patients with metabolic
   syndrome and an increased risk of certain malignancies [[39]8,[40]9].
   It has been shown that metabolic syndrome is positively associated with
   breast cancer and significantly associated with TNBC [[41]10].

   Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver
   disease intimately related to metabolic syndrome and abdominal obesity
   [[42]11,[43]12,[44]13]. It is becoming increasingly evident that NAFLD
   is not only linked to an increased risk of liver-related mortality or
   morbidity, but also associated with extrahepatic complications such as
   cardiovascular disease, chronic kidney disease, pulmonary
   insufficiency, and extrahepatic malignancies [[45]14,[46]15,[47]16]. A
   retrospective study by Nseir et al. [[48]17] indicated that NAFLD is
   associated with breast cancer independent of known risk factors.

   In addition, breast cancer patients often develop non-alcoholic fatty
   liver disease during the course of disease. Bilici et al. [[49]18]
   reported a prevalence of NAFLD as high as 63% and 72% in newly
   diagnosed and systematically treated breast cancer patients,
   respectively. It has also been reported that long-term selective
   estrogen receptor modulator (SERM) administration may increase the risk
   of NAFLD development, with fatty liver reported in 48.5% of
   tamoxifen-treated and 50.2% of toremifene-treated breast cancer
   patients at 60 months [[50]19]. It is of particular note that patients
   who present with NAFLD have been reported to have longer disease-free
   survival (DFS) [[51]20,[52]21], but the underlying mechanisms
   associated with improved clinical outcomes have not been thoroughly
   investigated.

   Recently, advances in sequencing technology and bioinformatics have
   made it possible to explore the pathogenesis of diseases and the
   interactions between different diseases at the gene level, which is
   expected to shed new light on the pathogenesis, diagnosis, and
   treatment of diseases [[53]22,[54]23,[55]24]. Here, we investigated the
   common differentially expressed genes (DEGs) of NAFLD and TNBC from
   public RNA-sequencing databases and identified 14 candidate hub genes
   most likely to mediate NAFLD and TNBC occurrence. Next, the biological
   functional pathways of the hub genes were estimated to explore the
   underlying mechanisms of both diseases. Finally, validation and
   prognostic analysis were performed in a new cohort of TNBC patients.

2. Materials and Methods

2.1. Study Design and Data Collection

   Three microarray datasets, [56]GSE63067 and [57]GSE48452 of NAFLD, and
   [58]GSE38959 of TNBC, were collected from the GEO
   ([59]http://www.ncbi.nlm.nih.gov/geo/, accessed on 1 November 2022)
   database. The nature of the three microarray datasets from the GEO
   database is summarized in [60]Table 1. Non-alcoholic steatohepatitis
   (NASH) is a stage of NAFLD that is usually associated with a worse
   prognosis [[61]25] and has a higher prevalence and more advanced stage
   of neoplasms compared to steatosis [[62]26]; thus, the NASH samples
   were selected as the representatives for analysis in this study. The
   [63]GSE63067 dataset included the gene expression profiles of 18
   samples, of which 9 were NASH patients and 7 were controls, and the
   [64]GSE38959 dataset included 47 samples, of which 30 were TNBC
   patients and 13 were controls. The [65]GSE48452 dataset consisted of
   human liver biopsy samples taken at different phases from control to
   NASH; 14 controls and 18 NASH samples were used in this study.

Table 1.

   The nature of the three microarray datasets from the GEO database.
   Series Country Status Platforms Type of Samples Numbers
   [66]GSE63067 Sweden Public on 7 November 2014 [67]GPL570 non-alcoholic
   steatohepatitis 9
   steatosis 2
   healthy 7
   [68]GSE48452 Germany Public on 8 August 2013 [69]GPL11532 non-alcoholic
   steatohepatitis 18
   steatosis 14
   healthy obese 27
   control 14
   [70]GSE38959 Japan Public on 21 December 2012 [71]GPL4133
   triple-negative breast cancer 30
   normal mammary ductal cells 13
   normal human vital organs including heart, lung, liver, and kidney 4
   [72]Open in a new tab

   RNAseq profiling in the form of fragments per kilobase million (FPKM)
   and clinicopathological breast cancer data were obtained from the TCGA
   ([73]https://portal.gdc.cancer.gov, accessed on 1 November 2022)
   database (TCGA-BRCA cohort). TNBC samples were selected according to
   the status of ER, PR, and HER2 by referring to the method of Craven et
   al. [[74]27]. One sample with an unknown ID was excluded, and samples
   from 132 patients were finally selected for analysis.

2.2. Differentially Expressed Gene (DEG) Selection

   DEGs were extracted and analyzed separately using the R package
   “limma”. The fold changes (FCs) were calculated for individual gene
   expression levels. Genes meeting specific cut-off criteria of p-value <
   0.05 and |logFC| > with [mean|logFC|) + 2 × sd(|logFC|)] were defined
   as DEGs. The overlapping DEGs between NAFLD and TNBC were delineated
   using the R package “ggVennDiagram”. These common DEGs with consistent
   upregulation or downregulation trends were retained for subsequent
   analysis.

2.3. Functional Classification and Pathway Enrichment of DEGs

   The above overlapping DEGs were submitted to Gene Ontology (GO)
   functional enrichment analysis, which consisted of biological process
   (BP), cellular component (CC), and molecular function (MF) analyses,
   and to Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway
   enrichment analysis using the R package “cluster Profiler”. The
   enriched GO terms and KEGG pathways with an adjusted p-value < 0.1 were
   selected.

2.4. Protein–Protein Interaction (PPI) Establishment and Hub Gene
Identification

   To further explore the interactions among the common genes obtained as
   described above, the Search Tool for the Retrieval of Interacting Genes
   (STRING) ([75]http://string-db.org/, accessed on 12 November 2022) was
   used for PPI network construction. Subsequently, Cytoscape software was
   used to visualize the PPI network. The Cytoscape plug-in Minimal Common
   Oncology Data Elements (MCODE,
   [76]http://apps.cytoscape.org/apps/mcode, accessed on 12 November 2022)
   was used to screen out key protein expression molecules and multiple
   topological analysis algorithms in the cytoHubba plug-in
   ([77]http://hub.iis.sinica.edu.tw/cytohubba/, accessed on 12 November
   2022), such as MCC, MNC, Degree, and EPC, were used to screen the hub
   genes in the PPI network.

2.5. Hub Gene Expression Validation and Prognostic Analysis

   The expression levels of the identified hub genes were validated in 132
   TNBC samples and 113 controls from the TCGA cohort, and 18 NASH samples
   and 14 controls from the GEO database. The Wilcoxon test was used to
   compare the data between the two groups, and a two-sided p-value < 0.05
   was considered significant. According to the median expression level
   for each gene, the TNBC samples were divided into high- or
   low-expression groups. Survival analysis was performed using univariate
   and multivariate Cox regression hazard analysis, providing hazard
   ratios (HRs) and 95% confidence intervals (CIs), and survival curves
   were derived using Kaplan–Meier (KM) survival analysis with log-rank
   tests for comparison.

2.6. Immune Infiltration Analysis

   The ssGSEA (single-sample gene set enrichment analysis) algorithm is a
   rank-based method that defines a score representing the degree of
   absolute enrichment of a particular gene set in each sample
   [[78]28,[79]29]. The ssGSEA score utilized
   immune-cell-marker-associated gene sets
   ([80]http://cis.hku.hk/TISIDB/data/download/CellReports.txt, accessed
   on 20 November 2022) to quantify the infiltration of immune cells in
   TNBC tissue and determine the level of immune infiltration in each
   sample. Pearson’s correlation analysis was used to reveal the
   relationships between hub genes and immune cells.

3. Results

3.1. DEG Identification in NASH and TNBC

   In the NASH and control groups in the [81]GSE63067 dataset, there were
   498 up-DEGs and 163 down-DEGs screened with a logFC threshold of 0.472
   ([82]Figure 1A). In the TNBC and control groups in the [83]GSE38959
   dataset, there were 453 up-DEGs and 422 down-DEGs screened with a logFC
   threshold of 1.834 ([84]Figure 1B). A Venn diagram was used to
   determine the intersection and 42 common DEGs were identified
   ([85]Figure 1C). After excluding genes with opposite expression trends,
   27 DEGs with the same expression trends were found, including 19 common
   upregulated genes and 8 common downregulated genes ([86]Table S1).

Figure 1.

   [87]Figure 1
   [88]Open in a new tab

   Characterization of the DEGs in NASH and TNBC. (A) Volcano map of DEGs
   between NASH samples and normal samples in [89]GSE63067; (B) volcano
   map of DEGs between TNBC samples and normal samples in [90]GSE38959;
   (C) Venn diagram of the common DEGS between the two upregulation and
   two downregulation modules in NAFLD and TNBC. DEGs, differentially
   expressed genes; NASH, non-alcoholic steatohepatitis; TNBC,
   triple-negative breast cancer.

3.2. GO and KEGG Enrichment Pathway Analysis of DEGs

   To better understand the biological functions of the identified DEGs,
   GO and KEGG pathway enrichment analyses were performed. After screening
   with the threshold of adjusted p < 0.1, significantly enriched GO terms
   and KEGG terms were selected.

   As shown in [91]Figure 2, in the BP category, DEGs were mainly enriched
   in maintenance of location, leukocyte cell–cell adhesion, leukocyte
   aggregation, leukocyte migration involved in inflammatory response,
   protein nitrosylation, peptidyl-cysteine S-nitrosylation, and
   regulation of apoptotic signaling pathway. In the CC category, DEGs
   were principally associated with secretory granule lumen, cytoplasmic
   vesicle lumen, vesicle lumen, immunological synapse,
   collagen-containing extracellular matrix, and nuclear inner membrane.
   The analysis of the MF category indicated that DEGs were enriched in
   toll-like receptor binding, fatty acid derivative binding, fatty acid
   binding, monocarboxylic acid binding, microtubule binding, integrin
   binding, and tubulin binding. Furthermore, two KEGG pathways with
   significant enrichment were the peroxisome proliferator-activated
   receptor (PPAR) signaling pathway and the IL-17 signaling pathway.

Figure 2.

   [92]Figure 2
   [93]Open in a new tab

   GO and KEGG analysis results of the common DEGs. GO, Gene Ontology; BP,
   biological process; CC, cellular component; MF, molecular function;
   KEGG, Kyoto Encyclopedia of Genes and Genomes.

3.3. PPI Network Construction and Hub Gene Identification

   To determine the interactions among the DEGs and identify hub genes,
   the PPI network of the DEGs was generated using STRING. With the aim of
   preventing important hub genes being missed, we modified the PPI
   settings to have a minimum required interaction score of medium
   confidence (0.400) and a maximum number of interactions of no more than
   20 interactors to increase the maximum number of interactions and the
   number of proteins directly related to the input proteins. Then, a PPI
   with 47 nodes and 122 edges, with a PPI enrichment p value < 1.0 ×
   10^−16, was obtained and imported into Cytoscape software v3.9.1 for
   visualization ([94]Figure 3A, [95]Table S2).

Figure 3.

   [96]Figure 3
   [97]Open in a new tab

   Construction of the PPI network and module analysis. (A) The
   visualization results of the PPI network of the common DEGs obtained
   from Cytoscape software v3.9.1; (B–D) three crucial clustering modules
   extracted by MCODE. PPI, protein–protein interaction; DEG,
   differentially expressed genes; STRING, Search Tool for the Retrieval
   of Interacting Genes; MCODE, Minimal Common Oncology Data Elements.

   The MCODE plug-in was used to conduct module analysis to detect crucial
   clustering modules. Three modules were retrieved from the PPI network.
   The criteria were set as follows: Degree Cutoff = 2, Node Score Cutoff
   = 0.2, K-Core = 2, and Max. Depth = 100. Module 1 included 10 nodes and
   84 edges with a cluster score (density times the number of members) of
   9.333. Module 2 and module 3 had 6 nodes and 20 edges and 3 nodes and 6
   edges, respectively, and the scores were 4.000 and 3.000, respectively
   ([98]Figure 3B–D).

   The CytoHubba plug-in was used to identify hub genes. Based on the MCC,
   MNC, Degree, and EPC algorithms, the top 15 important hub genes in the
   PPI networks were predicted. The intersection of these 15 genes from
   the four algorithms revealed 14 candidate hub genes: TLR4, CYBB, NCF1,
   NCF2, S100A8, S100A9, ITGB2, RAC2, ITGAM, CYBA, ICAM1, CXCL10, CXCR3,
   and ITGAL.

   Combined with the logFC values of the hub genes in the [99]GSE38959
   dataset, the GO and KEGG enrichment pathways were analyzed. The top 10
   GO terms and KEGG pathways are shown in [100]Figure 4. In the BP
   category, nine hub genes including CYBB, NCF1, NCF2, ITGB2, RAC2,
   ITGAM, CYBA, ICAM1, and TLR4 were enriched in reactive oxygen species
   metabolic process. Furthermore, S100A8, S100A9, ITGB2, RAC2, ITGAM,
   ICAM1, CXCL10, and CXCR3 were enriched in leukocyte migration. Enriched
   CC and MF were related to redox reactions like NADPH oxidase complex,
   superoxide-generating NADPH oxidase activity, and oxidoreductase
   activity. KEGG enrichment analyses showed that leukocyte
   transendothelial migration was highly correlated with these genes.

Figure 4.

   [101]Figure 4
   [102]Open in a new tab

   Functional enrichment analysis results of the hub genes. NADPH,
   nicotinamide adenine dinucleotide phosphate; RAGE, receptor for
   advanced glycation endproducts.

3.4. Hub Gene Expression Validation and Prognostic Analysis

   Validation was performed in the TCGA-BRCA cohort for TNBC and the
   [103]GSE48452 dataset for NASH. For TNBC, the differences in the
   expression levels of all hub genes between normal tissues and TNBC
   samples were statistically significant ([104]Figure 5A). Compared with
   normal tissues, 13 hub genes were upregulated, including CYBB, NCF1,
   NCF2, S100A8, S100A9, ITGB2, RAC2, ITGAM, CYBA, ICAM1, CXCL10, CXCR3,
   and ITGAL, and TLR4 was downregulated. For NASH, the expressions of hub
   genes ITGB2, RAC2, ITGAM, and CYBA were upregulated, and the changes in
   other genes’ expressions were not statistically significant
   ([105]Figure 5B).

Figure 5.

   [106]Figure 5
   [107]Open in a new tab

   Comparison of hub genes between normal tissues and TNBC samples in
   TCGA-BRCA cohort (A) and control group and NASH samples in the
   [108]GSE48452 dataset (B). TNBC, triple-negative breast cancer; TCGA,
   The Cancer Genome Atlas; BRCA, breast cancer; NASH, non-alcoholic
   steatohepatitis; TNBC, triple-negative breast cancer.

   To evaluate the clinical relevance of hub gene expression, the TNBC
   samples were divided into a high-expression group and a low-expression
   group according to the median expression level of each gene for
   prognostic analysis. Univariate Cox analysis suggested that high
   expression of ITGB2, RAC2, ITGAM, and CXCL10 was associated with better
   overall survival (OS) ([109]Table 2). The corresponding KM survival
   curves are shown in [110]Figure 6. In addition, two prognostic factors
   associated with worse OS were identified in terms of
   clinicopathological features, namely black or African American and
   Asian ethnicity, and N stage. All variables significant upon univariate
   Cox regression analysis (p ≤ 0.05) were subjected to multivariate Cox
   regression analysis, and it was found that the N stage was an
   independent risk factor for overall survival and the remaining factors
   were not significant.

Table 2.

   The Cox regression analysis results of the hub genes and
   clinicopathological variables in the TCGA-BRCA group.
   Factor Univariate Cox Regression Analysis Multivariate Cox Regression
   Analysis
   HR (95%CI) p-Value HR (95%CI) p-Value
   TLR4 0.640 (0.237–1.728) 0.378
   CYBB 0.509 (0.183–1.411) 0.194
   NCF1 0.431 (0.155–1.200) 0.107
   NCF2 0.622 (0.228–1.694) 0.352
   S100A8 0.810 (0.295–2.226) 0.683
   S100A9 0.523 (0.181–1.507) 0.230
   ITGB2 0.157 (0.044–0.556) 0.004 0.213 (0.033–1.376) 0.104
   RAC2 0.341 (0.118–0.984) 0.047 1.067 (0.282–4.036) 0.924
   ITGAM 0.282 (0.094–0.842) 0.023 1.392 (0.318–6.100) 0.661
   CYBA 1.750 (0.632–4.847) 0.282
   ICAM1 0.676 (0.250–1.828) 0.440
   CXCL10 0.244 (0.082–0.725) 0.011 0.430 (0.108–1.718) 0.232
   CXCR3 0.419 (0.152–1.156) 0.093
   ITGAL 0.413 (0.148–1.151) 0.091
   Age 0.773 (0.249–2.406) 0.657
   Race 2.830 (1.019–7.860) 0.046 2.090 (0.631–6.922) 0.227
   T stage
   (T2 vs. T1,
   T3/T4 vs. T1) 1.717 (0.471–6.255) 0.194
   4.427 (0.858–22.838)
   N stage
   (N1/N2/N3
   vs. N0) 5.641 (1.815–17.534) 0.003 4.681 (1.452–15.089) 0.010
   [111]Open in a new tab

Figure 6.

   [112]Figure 6
   [113]Open in a new tab

   Kaplan–Meier survival curves of association between the expression
   levels of hub genes and the OS of TNBC patients (group cutoff =
   median). OS, overall survival; TNBC, triple-negative breast cancer.

3.5. Association between the Hub Genes and Immune Infiltration

   [114]Figure 7 shows the relationships between 14 hub genes and 27
   immune cells (results for central memory CD4 T cells unavailable)
   according to the results of ssGSEA analysis. For TNBC samples from the
   [115]GSE38959 dataset, NCF2, ICAM1, and CXCL10 were significantly
   associated with activated CD8 T cells (NCF2, r = 0.764, p = 2.42 ×
   10^−9; ICAM1, r = 0.705, p = 1.32 × 10^−7; CXCL10, r = 0.802, p = 1.03
   × 10^−10) and activated CD4 T cells (NCF2, r = 0.715, p = 7.16 × 10^−8;
   ICAM1, r = 0.804, p = 8.70 × 10^−11; CXCL10, r = 0.785, p = 4.72 ×
   10^−10). Specifically, NCF2, CXCL10, and CYBB were correlated with
   regulatory T cells (NCF2, r = 0.773, p = 1.22 × 10^−9; CXCL10, r =
   0.730, p = 2.86 × 10^−8; CYBB, r = 0.718, p = 6.02 × 10^−8), and
   myeloid-derived suppressor cells (MDSCs) (NCF2, r = 0.743, p = 1.19 ×
   10^−8; CXCL10, r = 0.79, p = 4.24 × 10^−10; CYBB, r = 0.733, p = 2.29 ×
   10^−8). In addition, ITGB2 was associated with activated B cells (r =
   0.766, p = 2.22 × 10^−9) and immature B cells (r = 0.733, p = 2.36 ×
   10^−8).

Figure 7.

   [116]Figure 7
   [117]Open in a new tab

   Associations between the hub genes and immune infiltration in the
   [118]GSE38959 group (A) and genes significantly associated with
   activated CD8 T cells, activated CD4 T cells, regulatory T cells and
   myeloid-derived suppressor cells (B). Red: positive correlation; blue:
   negative correlation.

4. Discussion

   In recent years, the relationship between NAFLD and breast cancer has
   become a research hotspot, and an increasing number of studies have
   confirmed the correlation. Some studies have reported that breast
   cancer is a common extrahepatic complication of NAFLD
   [[119]16,[120]30]. Simultaneously, it also has been suggested that
   patients with breast cancer, especially those receiving endocrine
   therapy, present an increased risk of NAFLD [[121]31,[122]32]. Based on
   these combined results, NAFLD may be related to the occurrence and
   progression of breast cancer. In addition, it has been proposed that
   liver metastasis in the diagnosis of fatty liver patients with breast
   cancer is significantly lower than that of patients with normal liver
   histology, further revealing the correlation between the two events in
   clinical practice [[123]33]. However, these studies have been mostly
   observational, and the mechanism connecting NAFLD and TNBC remains
   unclear to date. Therefore, exploring the molecular mechanisms to
   enable early identification and intervention is undoubtedly of great
   clinical significance.

   In this study, we explored common DEGs of NASH and TNBC datasets in
   public databases through bioinformatics analysis and observed the
   biological processes and signaling pathways in which they jointly
   participate. GO and KEGG enrichment analyses of the common DEGs showed
   enrichment in leukocyte aggregation, migration and adhesion, apoptosis
   regulation, and the PPAR signaling pathway, suggesting that TNBC in
   NAFLD patients was likely due to enhanced leukocyte recruitment in the
   inflammatory response and abnormal apoptosis. Interestingly, the PPAR
   signaling pathway not only controls the expression of genes encoding
   proteins of lipid metabolism, but is also involved in anti-cancer
   responses [[124]34]. One of the mechanisms by which PPARs act to
   control cancer progression is to affect the NF-κB signaling pathway, or
   its upstream pathways, such as the Toll-like receptor 4 (TLR4)
   signaling pathway [[125]35,[126]36]. PPAR γ agonists have been found to
   induce apoptosis in TNBC cells and inhibit melanoma progression in mice
   [[127]37,[128]38].

   A total of 14 candidate hub genes most likely to mediate NASH and TNBC
   occurrence were identified, including TLR4, CYBB, NCF1, NCF2, S100A8,
   S100A9, ITGB2, RAC2, ITGAM, CYBA, ICAM1, CXCL10, CXCR3, and ITGAL.

   CYBB, CYBA, NCF1, NCF2, and RAC2 are NADPH oxidase (NOX) subunit genes
   and are associated with inflammation and fibrosis in multiple organs,
   such as the liver [[129]39,[130]40], lungs [[131]41], and kidneys
   [[132]42], as well as with various types of cancer [[133]43]. NOX can
   produce reactive oxygen species (ROS) that cause changes in cellular
   redox status, leading to chronic liver injury and fibrosis, which is
   critical for alcoholic steatohepatitis and NASH [[134]44,[135]45]. The
   analysis of the TCGA cohort showed that NOX-related genes were
   expressed more highly in tumor cells than in normal tissues of the same
   tissue origin, which suggested that the abnormal expression and
   regulation of NOX may be related to tumorigenesis and the increase of
   ROS in tumor cells [[136]46], which probably contributes to the
   increased susceptibility of TNBC patients to NAFLD compared to the
   healthy population. In addition, RAC2 was strongly associated with OS
   in patients. RAC2 is a 21 kDa RAS superfamily of GTPases that stabilize
   the cytoskeleton structure of actin [[137]47,[138]48]. Chen et al.
   [[139]49] found the high expression of RAC2 can inhibit the
   proliferation of breast cancer cells.

   ITGAM, ITGB2, and ITGAL are involved in the most common integrins
   expressed on leukocytes, including Mac-1 (αMβ2 or CD11b/CD18) and
   leukocyte function-related antigen 1 (LFA-1 or αLβ2) [[140]50,[141]51].
   Activated integrins play a crucial role in trafficking immune cells
   into tissues, activating and promoting the proliferation of effector
   cells, and inducing the formation of immune synapses between cells
   [[142]52,[143]53]. Clinically, Mac-1 expression is increased in
   patients with metabolic syndrome [[144]54]. It has also been confirmed
   that Mac-1 is required for pro-inflammatory gene expression by
   macrophages in adipose tissue inflammation and is related to recruiting
   monocytes from bone marrow and inducing them to transform into M1-like
   macrophages (pro-inflammatory and usually anti-tumor) to express
   cytotoxic factors to engulf and destroy tumor cells
   [[145]55,[146]56,[147]57]. Rojas et al. [[148]51] demonstrated that an
   integrin marker composed of ITGA4, ITGB2, ITGAX, ITGB7, ITGAM, ITGAL,
   and ITGA8 had the potential to recognize basal-like breast cancers with
   immune-infiltrating and favorable prognosis. Our results are similar to
   this finding, with ITGAM and ITGB2 highly expressed in both diseases
   and associated with a better prognosis in TNBC. Further analysis of
   immune infiltration showed a positive correlation between integrin
   genes and activated B cells and immature B cells.

   CXCL10 and its homologous receptor CXCR3 are critical in the
   development of specific features of the NAFLD phenotype, wherein they
   are mainly involved in the induction of inflammation, regulation of
   adipogenesis and oxidative stress, and other related processes
   [[149]58,[150]59]. In the process of tumor progression, studies have
   shown that CXCL10 has a dual role. It can not only promote tumor
   progression by increasing cell proliferation and metastasis, but also
   exert an anti-malignancy function by inhibiting angiogenesis and
   influencing the tumor microenvironment [[151]60,[152]61,[153]62]. Sun
   et al. [[154]63] found that CXCL10 expression was significantly
   upregulated in mice with melanoma and that CXCL10 promoted the
   proliferation of monocyte-like MDSCs, leading to an immunosuppressive
   microenvironment. On the other hand, it has also been demonstrated that
   tumor-cell-derived CXCL9/CXCL10 regulates the recruitment of T cells in
   various tumors [[155]64,[156]65,[157]66]. Our study suggested that
   CXCL10 is positively correlated with MDCSs and activated T cells, and
   TNBC patients with high CXCL10 expression obtained a better prognosis.
   Therefore, in the context of NAFLD, CXCL10 may play an anti-tumor role
   in TNBC, but more in-depth experimental research is still needed.

   Although many studies have linked metabolic syndrome to the development
   of cancer and poor prognosis, it may be a symptom of a general
   metabolic disorder. Our study explored the relationship between NAFLD
   and TNBC at the genetic level for the first time, and found that the
   hub genes ITGB2, RAC2, and ITGAM were upregulated in both diseases and
   were prognostic protective factors in TNBC. This is inconsistent with
   our understanding of risk factors such as obesity, a high-fat diet, and
   NAFLD that promote the occurrence and progression of breast cancer.
   Therefore, further experimental studies will be of great significance
   and are expected to find new targets for diagnosis, prognostic
   assessment, and treatment of TNBC.

   The study had several limitations. First, although the role of these
   genes has been elucidated in multiple studies, the key pathways and hub
   genes identified have not been validated in experiments. Second, due to
   the lack of a dataset, the validation of the hub gene was performed in
   patients with only NAFLD or TNBC, but not in patients with NAFLD
   combined with TNBC. Third, the relationship between the hub genes and
   the prognosis of TNBC patients needs to be confirmed by prospective
   clinical studies.

5. Conclusions

   In conclusion, this study explored the hub genes of NAFLD and TNBC and
   illustrated the possible mechanisms for the co-occurrence trend of
   these two diseases. Redox reactions regulated by the NOX subunit genes
   and the transport and activation of immune cells regulated by integrins
   may play a central role in the development of NAFLD and TNBC.
   Additionally, the expressions of ITGB2, RAC2, ITGAM, and CXCL10 were
   significantly correlated with a good prognosis in TNBC and may be
   potential therapeutic targets for the development of gene therapies for
   TNBC patients with NAFLD.

Acknowledgments