Abstract

Object

   There is mounting clinical evidence that an increase in obesity is
   linked to an increase in cancer incidence and mortality. Although
   studies have shown a link between obesity and colon cancer, the
   particular mechanism of the interaction between obesity and colon
   cancer in females remains unknown. The goal of this work is to use
   bioinformatics to elucidate the genetic link between obesity and colon
   cancer in females and to investigate probable molecular mechanisms.

Methods

   [37]GSE44076 and [38]GSE199063 microarray datasets were obtained from
   the Gene Expression Omnibus (GEO) database. In the two microarray
   datasets and healthy controls, the online tool GEO2R was utilized to
   investigate the differential genes between obesity and colon cancer.
   The differential genes (DEGs) identified in the two investigations were
   combined. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway enrichment studies were performed on the DEGs.
   The STRING database and Cytoscape software were then used to build
   protein-protein interaction (PPI) networks to discover hub genes.
   NetworkAnalyst was also used to build networks of target microRNAs
   (miRNAs) and hub genes, as well as networks of transcriptions.

Results

   Between the two datasets, 146 DEGs were shared. The DEGs are primarily
   enriched in inflammatory and immune-related pathways, according to GO
   analysis and KEGG. 14 hub genes were identified via PPI building using
   the Cytoscape software’s MCODE and CytoNCA plug-ins: TYROBP, CD44, BGN,
   FCGR3A, CD53, CXCR4, FN1, SPP1, IGF1, CCND1, MMP9, IL2RG, IL6 and CTGF.
   Key transcription factors for these hub genes include WRNIP1, ATF1,
   CBFB, and NR2F6. Key miRNAs for these hub genes include hsa-mir-1-3p,
   hsa-mir-26b-5p, hsa-mir-164a-5p and hsa-mir-9-5p.

Conclusion

   Our research provides evidence that changed genes are shared by female
   patients with colon cancer and obesity. Through pathways connected to
   inflammation and the immune system, these genes play significant roles
   in the emergence of both diseases. We created a network between hub
   genes and miRNAs that target transcription factors, which may offer
   suggestions for future research in this area.

   Keywords: obesity, colon cancer, women, GEO, genes

Introduction

   In developed nations, a considerable fraction of the population is
   overweight or obese. Body mass index (BMI), which employs ranges of
   18.5-24.9 for normal, 25-29.9 for overweight, and 30 for obesity, is
   typically used to measure obesity ([39]1). Obesity prevalence has
   significantly increased during the previous ten to fifteen years and
   has already reached epidemic levels, affecting 2 billion individuals
   ([40]2, [41]3). Due to an increase in caloric intake and a decrease in
   physical activity, there is a persistent rise in the incidence of
   related cancers. This is thought to be the cause of the ongoing rise in
   the obese and overweight population ([42]4). Obesity is more prevalent
   in women than in males ([43]5), and up to 20% of cancer deaths in women
   are thought to be attributable to it ([44]6).

   In developed countries, colon cancer (CC) is the second most prevalent
   malignant disease ([45]7). Colonic epithelial cells, which are
   positioned in the organ’s lumen and self-renew every five days from
   cryptic stem cells at the base of the colonic epithelial cell crypt,
   are the source of CC ([46]8). There may be a variety of variables that
   affect cellular self-renewal and lead to the formation of CC. These
   variables include one’s way of life, food and genetic alterations
   ([47]9), which account for 15–30% of cases of CC and are brought on by
   oncogene overexpression and tumor suppressor gene inactivation
   ([48]10). There is still a sizable number of CCs that are irregular and
   linked to environmental or lifestyle variables. Patients lack a
   definite genetic susceptibility ([49]11, [50]12). CC is the most
   prevalent type of nutrition-related cancer in the general population,
   according to research ([51]13). Hypertrophic expansion of fat adipose
   tissue brought on by overeating shares many characteristics with solid
   tumor growth. Obese people’s adipose tissue contains adipocyte
   precursor cells that accelerate tumor growth by producing more
   endothelial precursor cells, pericytes and adipocytes, which results in
   angiogenesis and the growth of cancer cells in living organisms
   ([52]14).

   Obese individuals have hyperinsulinemia, insulin resistance, hormonal
   dysregulation, a chronic inflammatory state and a dysregulated energy
   metabolism. These modifications put obese people at higher risk for
   cancer and a poor prognosis ([53]15, [54]16). Adipose tissue is
   abundant in the tumor mesenchyme and adipocytes operate as endocrine
   cells that secrete signaling molecules such adipokines,
   pro-inflammatory cytokines and pro-angiogenic proteins to aid in the
   development and growth of tumors ([55]17). For instance, through
   activation of the prostaglandin and insulin-like growth factor
   pathways, long-term persistent chronic inflammation and insulin
   resistance increase colon cell proliferation and block programmed cell
   death or apoptosis. The Wnt pathway is particularly rich in proteins
   involved in the control of cell polarity, mitotic activity and
   fertility, which is closely associated with colon carcinogenesis
   ([56]18). Additionally, visceral adipocytes released greater amounts of
   lactate dehydrogenase ([57]19), interleukin (IL)-6, IL-8 and tumor
   necrosis factor alpha (TNF). These inflammatory substances activate
   signaling pathways associated with NF-κB, STAT3, phosphatidylinositol
   3-kinase/threonine kinase Akt (PI3K/Akt), mechanistic target of
   rapamycin/mitogen-activated protein kinase (mTOR/MAPK/p38) and other
   pro-inflammatory pathways. The development of metastases and associated
   cancer cell proliferation, invasion, angiogenesis and cell survival are
   all mediated by the activation of these pathways ([58]4,
   [59]20–[60]22). In contrast, while most clinical investigations have
   identified a significant link between weight and CC risk in males,
   there has been little to no evidence of this relationship in women
   ([61]23–[62]27). Therefore, it is crucial to research the relationship
   between female obesity and CC. Understanding the genetic relationships
   between CC and obesity may offer crucial mechanistic insights into CC
   in the setting of female obesity.

   With the advancement of information technology in biology, increased
   gene screening in a genomic context has been made possible in recent
   years. To determine the differences between CC and obesity, this work
   employed two original microarray datasets retrieved from Gene
   Expression Omnibus (GEO, [63]http://www.ncbi.nlm.nih.gov/geo). The
   detected DEGs were then examined using gene ontology (GO) analysis,
   KEGG pathway analysis and protein-protein interaction (PPI) analysis.
   Then, using the Cytoscape program, we predicted the probable
   transcription factors and miRNAs of hub genes and created a network
   that displayed the interactions between transcription factors, miRNAs
   and genes.

Materials and methods

Data download

   The two datasets mentioned above were downloaded from the GEO website
   ([64]https://www.ncbi.nlm.nih.gov/geo/). For the [65]GSE44076 dataset,
   we chose 27 female CC patients and 23 healthy female controls; for the
   [66]GSE199063 dataset, we chose 50 female obese patients and 28 healthy
   female controls. [67]Table 1 displays the dataset’s specifics. Without
   further authorization, the data were downloaded from the official
   platform.

Table 1.

   Details of the two dataset clusters.
   Diseases Serial number Platform Tissue Source Patient group Control
   group Total number
   Colon cancer [68]GSE44076 [69]GPL13667 tumor samples healthy colon
   mucosae 27 23 50
   obesity [70]GSE199063 [71]GPL23126 Adipose tissue 50 28 78
   [72]Open in a new tab

   [73]GSE44076: normal adjacent mucosa and tumor samples from 98
   individuals and 50 healthy colon mucosae. However, we only selected
   female patients who met the criteria for our study. [74]GSE199063:
   adipose tissue from a cohort of obese women. Adipose tissue biopsies
   were obtained from the control group(n = 28),before RYGB(n = 50), and
   then 2(n = 49) and 5 years (n = 38)thereafter.

Screening of differential genes between the two datasets

   Using the online biometric analysis tool GEO2R
   ([75]https://www.ncbi.nlm.nih.gov/geo/geo2r/), which is available on
   the official GEO website, differentially expressed genes in the two
   datasets were analyzed. Volcano plots were downloaded to further screen
   the DEGs between the two datasets with a screening threshold of|log2 FC
   | ≥ 1, P < 0.05. For overlapping DEGs between female obesity and female
   CC, the resulting differential genes between the two datasets were
   visualized using Venn plots from the Venn online platform
   ([76]http://bioinformatics.psb.ugent.be/webtools/Venn/). The analysis
   of the obtained overlapped DEGs then continued.

Functional enrichment analysis of DEGs

   With the use of the R program, the aforementioned overlapping DEGs were
   analyzed for GO enrichment, including biological process (BP), cellular
   component (CC) and molecular function (MF) with an adjusted P value <
   0.05.

Pathway enrichment analysis of DEGs

   The KEGG pathway enrichment analysis of the aforementioned overlapping
   DEGs was performed using the R package with an adjusted P value < 0.05.

Protein interaction network analysis of DEGs

   To further examine the relationships between DEGs, the aforementioned
   list of overlapping DEGs was imported into the STRING
   ([77]http://string-db.org) database. To download the list of protein
   mediators and subsequently use Cytoscape software to display and
   analyze the PPI network, an interaction score of at least 0.4 was
   deemed significant. Key protein expression molecules were screened
   using the Cytoscape plugin Minimal Common Oncology Data Elements
   (MCODE), and PPI network was obtained. The PPI network was screened for
   hub genes using the number-centered method with the CytoNCA plug-in.
   The 14 genes that overlap between the two calculations made by the
   plug-ins were then subjected to the next step of study.

MiRNAs and transcription factors associated with hub genes

   The miRNAs, transcription factors, and miRNAs-transcription
   factors-gene connections of hub genes were created using the
   NetworkAnalyst program (version 3.0, available at
   [78]https://www.networkanalyst.ca/). Cytoscape software was used to
   view and evaluate the data.

Results

Identification of DEGs in obesity and colon cancer

   From the NCBI GEO database ([79]https://www.ncbi.nlm.nih.gov/geo/), we
   retrieved the [80]GSE199063 dataset on obesity and the [81]GSE44076
   dataset on CC. 355 DEGs were found in the [82]GSE199063 dataset and
   3135 DEGs were found in the [83]GSE44076 dataset after modifying the
   threshold screening for P value < 0.05 and |log2 FC | >1.0. The GEO 2R
   web tool Volcano Plot was used to visualize the data results. The
   overlapped 146 genes were then obtained by intersecting the two
   datasets. The data outcomes were shown graphically in [84]Figure 1 .

Figure 1.

   [85]Figure 1
   [86]Open in a new tab

   Volcano and Venn plots of differentially expressed genes. (A), genes
   differentially expressed between obese and control in the [87]GSE199063
   dataset (red dots represent up-regulated genes, blue dots represent
   down-regulated genes). (B), genes differentially expressed between
   colon cancer and healthy control in the [88]GSE44076 dataset (red dots
   represent up-regulated genes, blue dots represent down-regulated
   genes). (C), Number of genes overlapping differentially expressed genes
   in the above two datasets. (CC, colon cancer).

GO analysis of overlapping DEGs

   We used the cluster Profiler tool in R to analyze the GO enrichment
   function of overlapping DEGs in order to understand the function of
   these genes. The molecular function (MF), biological process (BP) and
   cellular component (CC) make up the three parts of the GO enrichment
   function. After the P < 0.05 screening adjustment, the following
   results were obtained: BP functions mainly include positive regulation
   of cell adhesion, positive regulation of leukocyte activation, positive
   regulation of cell activation, positive regulation of lymphocyte
   activation, regulation of leukocyte cell-cell adhesion, positive
   regulation of leukocyte cell-cell adhesion, positive regulation of T
   cell activation, positive regulation of T cell adhesion. CC functions
   mainly include collagen-containing extracellular matrix, external side
   of plasma membrane, endoplasmic reticulum lumen, secretory granule
   membrane, collagen trimer, basement membrane, MHC class II protein
   complex, MHC protein complex. MF functions mainly include extracellular
   matrix structural constituent, immune receptor activity, cytokine
   activity, extracellular matrix binding, collagen binding, wnt-protein
   binding, proteoglycan binding and IgG binging. ([89] Figure 2 ;
   [90]Supplementary Figure 2S1 , [91]Supplementary Figure 2S2 ,
   [92]Supplementary Table S1 )

Figure 2.

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

   Bubble plot of GO enrichment analysis results. x-axis indicates the
   proportion of genes per functional term. y-axis indicates the annotated
   terms of gene enrichment. The circle size represents the number of
   genes: the larger the circle, the higher the number of genes. The
   circle color represents the adjusted P value: the redder the color, the
   higher the degree of gene enrichment. MF, Molecular Function; BP,
   Biological Process; CC, Cellular Component.

KEGG analysis of overlapping DEGs

   We evaluated the KEGG enrichment function of these genes using the
   cluster Profiler program in R software in order to comprehend the
   enrichment pathway of overlapping DEGs. The following outcomes were
   attained following adjustment of the P < 0.05 screen: Tuberculosis,
   Staphylococcus aureus infection, Phagosome, Cytokine-cytokine receptor
   interaction, HIF-1 signaling pathway, Systemic lupus erythematosus,
   Influenza A, Leishmaniasis, Inflammatory bowel disease, Rheumatoid
   arthritis, Fc gamma R-mediated phagocytosis, Hematopoietic cell
   lineage, Viral protein interaction with cytokine and cytokine receptor,
   Th17 cell differentiation, Intestinal immune network for IgA
   production, Graft-versus-host disease, Viral myocarditis, Asthma,
   Aldosterone-regulated sodium reabsorption, Allograft rejection. ([95]
   Figures 3 , [96]4 ; [97]Supplementary Figure 3S1 , [98]Supplementary
   Figure 3S2 , [99]Supplementary Table S2 ).

Figure 3.

   [100]Figure 3
   [101]Open in a new tab

   Bubble plot of KEGG enrichment analysis results. x-axis indicates the
   proportion of genes per functional term. y-axis indicates the annotated
   terms of gene enrichment. The circle size represents the number of
   genes: the larger the circle, the higher the number of genes. The
   circle color represents the adjusted P value: the redder the color, the
   higher the degree of gene enrichment.

Figure 4.

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

   Network diagram of KEGG enrichment analysis results. The line segments
   connect genes and enrichment pathways, and different colors represent
   different enrichment pathways. The size of the circles represents the
   different number of connected line segments, the larger the circle, the
   more genes and pathways are connected, the gray circle represents
   genes, the yellow circle represents pathways.

PPI network construction of overlapping DEGs and hub genes identification

   To identify the gene connections, the overlapping DEGs were imported
   into the STRING database ([104] Supplementary Figure 5S1 ). Data
   findings were acquired, and Cytoscape software was used to perform a
   visual analysis. The Cytoscape plugin cytoNCA was used to examine the
   PPI network and find hub genes. ([105] Figure 5 ). We examined the top
   30 genes that might be crucial genes using the Median center algorithm:
   FN1, PTPRC, IL6, FASN, CCND1, IGF1, Tyrosine protein tyrosine kinase
   binding protein (TYROBP), PHGDH, HPGDS, MMP9, SGK1, BGN, HADH, CD44,
   CXCR4, CTGF, FCGR3A, SPP1, CD53, CYP27A1, SFRP2, COL12A1, ALDH6A1,
   IL2RG, ABCC3, ADAMTS4, ALCAM, RGS1, CD248, NEDD4L. In the meantime, we
   used the Cytoscape plugin MCODE to analyze the PPI network in order to
   find relevant gene cluster modules and calculate cluster scores (filter
   criteria: degree cut-off = 2, node score cut-off = 0.2, k-core = 2, max
   depth = 100). Five modules were obtained and four of them had module
   scores of at least four ([106] Figure 6 ). We eliminated the top 30
   candidates for hub genes: CXCR4, CTGF, FN1, IL6, CCND1, MMP9, SPP1,
   CD44, IGF1, CTGF, MMP7, EDN1, BGN, FCGR3B, TIMP1, SERPINE1, CD53, AIF1,
   CCL19, CD209, MPEG1, IL2RG, CCL5, MRC1, IL1RN, TYROBP, IL18, FCGR3A,
   FCGR1A, CC1QC.14 hub genes were produced when we combined the hub genes
   from cytoNCA and MCODE: TYROBP, CD44, BGN, FCGR3A, CD53, CXCR4, FN1,
   SPP1, IGF1, CCND1, MMP9, IL2RG, IL6, CTGF.

Figure 5.

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

   The protein interaction network obtained from the analysis of PPI with
   Cytoscape plugin cytoNCA, the circles represent the proteins and the
   lines connect the interacting proteins.

Figure 6.

   [109]Figure 6
   [110]Open in a new tab

   The protein interaction network obtained by analyzing PPI with
   Cytoscape plugin MCODE, (A–D) represent the four sub-modules obtained
   by MCODE plugin.

Target transcription factors prediction

   To forecast the hub genes’ target transcription factors, we used the
   Network Analyst database. Cytoscape software was used to import the
   obtained data results for visual analysis. In [111]Figure 7 and
   [112]Table 2 , we predicted the transcription factors of hub genes,
   where the transcription factor WRNIP1 interacts with 5 core genes,
   including CD44, CD53, IL2RG, IGF1, and CXCR4. Transcription factors
   ATF1, CBFB and NR2F6 interacted with 4 core genes, including
   transcription factor ATF1 interacting with FCGR3A, TYROBP, IGF1 and
   FN1. The transcription factor CBFB interacts with CD44, TYROBP, IGF1
   and CXCR4. The transcription factor NR2F6 interacts with IGF1, CCND1,
   CD53 and FN1.The transcription factor ZNF24 interacts with CD44, TYROBP
   and CD53. The transcription factor NR2C2 interacts with IL2RG, TYROBP
   and CD53. The transcription factor SMARCE1 interacts with IL2RG, IGF1
   and FCGR3A. The transcription factor GABPA interacts with FCGR3A, IGF1
   and CD53. The transcription factor ARID1B interacts with FCGR3A, TYROBP
   and IL6. Transcription factor TFDP1 interacts with FCGR3A, CCND1 and
   CD53. The transcription factor MAZ interacts with IL2RG, CCND1 and
   MMP9. The transcription factor MLLT1 interacts with FN1, CCND1 and
   CXCR4. The transcription factors POLR2H and ZNF644 both interact with
   IL6, FN1 and CD53. The transcription factor ZBTB26 interacts with MMP9,
   FN1 and CCND1. The transcription factor KLF16 interacts with IL2RG, FN1
   and SPP1. Transcription factor KLF9 interacts with BGN, TYROBP and FN1.
   The transcription factor EZH2 interacts with CXCR4, CCND1 and MMP9.
   Transcription factor TRIM22 interacts with CD44, CCND1 and TYROBP. The
   transcription factor MTA2 interacts with CD53, TYROBP and CXCR4.

Figure 7.

   [113]Figure 7
   [114]Open in a new tab

   Transcription factor-gene network of 12 hub genes, red circles
   represent hub genes and blue diamonds represent transcription factors.

Table 2.

   The transcription factors of identified hub genes.
   Identified genes Transcription factors
   CCND1 TFE3, MTA1, MBD2, SOX13, HBP1, MXD3, ELF3, KDM5B, NFIA, CREM,
   MBD1, MYC, ZNF423, ARID4B, TFDP1, KLF11, ETS1, MAZ, SP2, EZH2, GTF2F1,
   RFXANK, THRB, CTBP2, GLIS1, ZFX, MLLT1, CHD1, NR2F6, SAP30, SMC3, RFX1,
   TRIM22, TEAD1, PPARG, ZBTB26, PHF8
   CXCR4 EZH2, MLLT1, TCF7, GATA2, L3MBTL2, SUZ12, MYB, WRNIP1, TBX21,
   MTA2, SUPT5H, HDAC2, CBFB, TRIM28, HDAC6, SP3, EGR2, EED
   BNG ZBTB33, KLF9, GLIS2
   FN1 KLF9, SOX13, MBD1, ARID4B, KLF11, MLLT1, NR2F6, TEAD1, PPARG,
   ZBTB26, BCL6, DRAP1, NFIL3, TEAD3, RXRB, NFYC, ZHX2, ZNF197, MLX,
   DMAP1, ZNF324, ETV4, CEBPG, FOSL1, TGIF2, HHEX, GATAD2A, TFAP4, GATA4,
   ZNF580, GATAD1, ATF1, HMG20B, POLR2H, ZNF384, RARA, RERE, BATF, SSRP1,
   KLF16, ZNF644, ATF2, ZFP2, ATF4, CREB1
   IGF1 NR2F6, WRNIP1, SUPT5H, CBFB, ATF1, MAFF, FOXA2, RAD21, EP300,
   MYBL2, TEAD4, SMARCE1, CEBPD, MBD4, HNF4G
   IL6 CREM, POLR2H, ZNF644, ARID1B, IRF1, MIXL1, CREB3, NRF1, GTF2A2,
   PTTG1, RNF2, RUNX1, BACH1, NFRKB, ELK1, PTRF, MEF2D, ZNF512, ZNF584,
   ZNF175, ZNF589
   MMP9 MTA1, MXD3, MAZ, EZH2, RFXANK, CTBP2, ZBTB26, DRAP1, HHEX, TFAP4,
   GATAD1, SSRP1, KLF16, CTCF, MXD4, CUX1, KDM5A, ID3, ZNF7, ZKSCAN1,
   KDM1A, ZNF341
   CD44 KDM5B, SAP30, TRIM22, PHF8, EED, EBF1, RBBP5, TCF7, NFIC, CBFB,
   ZNF24, SIN3A, ZNF2, TARDBP, WRNIP1
   CD53 TFDP1, ZFX, NR2F6, ZNF24, WRNIP1, LEF1, ZBTB11, ELF1, HMBOX1,
   GATA2, KLF6, CUX1, SPI1, HDGF, GTF2E2, RAD51, ZNF644, MTA2, POLR2H,
   NR2C2, IRF1, ZNF175, GABPA, NR4A1, ZNF589, RUNX3
   FCGR3A TFDP1, GABPA, BDP1, TAL1, ZNF558, POLR3A, SMARCE1, ATF1, POLR2A,
   CREB3, BRF1, SMARCA4, SIRT6, SREBF2, DPF2, KLF13, TBP, TSHZ1, ATF3,
   ARID1B, ZNF407, ID3, ARNT, ZNF101, TRIM24, PBX2, MEF2D, RELA
   SPP1 CEBPG, KLF16, FOXJ2, CEBPA, SMAD4
   TYROBP ZBTB33, KLF9, ZNF423, TRIM22, TBX21, MTA2, CBFB, ATF1, ARID1B,
   IRF4, FOXM1, EGR1, ZNF24, ZNF101, KLF7, HDGF, CHD4, ZNF641, ZNF264,
   ZNF121, ZNF366, ZBTB1, ZNF239, NR2C2, ZNF501, ZNF547, TSHZ1, GFI1B,
   ZNF585B, MYNN, ZNF76, BCL11A, ZNF610, ZNF394, ZFP37, HIC1, BCL11B,
   ZBTB7A, EBF1, ZBTB17, ELF1, ZNF18, HMGN3, ZNF623, ZNF146, TARDBP,
   INSM2, ZEB1, ZNF8, KLF8
   [115]Open in a new tab

Target miRNAs prediction

   To forecast the hub genes’ target transcription factors, we used the
   Network Analyst database. Cytoscape software was used to import the
   obtained data results for visual analysis. In [116]Figure 8 and
   [117]Table 3 , we predicted the transcription factors of hub genes,
   where the transcription factor WRNIP1 interacts with 5 core genes,
   including CD44, CD53, IL2RG, IGF1, and CXCR4.hsa-mir-26b-5p interacted
   with 4 central genes, including FN1, CCND1, CXCR4 and IGF1.
   hsa-mir-164a-5p interacted with 4 central genes, including IL6, SPP1,
   CCND1 and CXCR4. hsa-mir-9-5p interacts with 4 central genes, including
   IL6, MMP9, CCND1 and CXCR4. In addition, hsa-mir-98-5p interacts with
   IL6, CCND1 and IGF1. hsa-mir-204-5p interacts with MMP9, CD44 and
   CXCR4. hsa-mir-204-5p interacts with MMP9, CD44 and CXCR4. interacts
   with MMP9, CD44 and CXCR4. hsa-mir-211-5p interacts with MMP9, CD44 and
   CXCR4. hsa-mir-16b-5p interacts with MMP9, CD44 and CCND1.
   hsa-let-7e-5p interacts with MMP9, IGF1 and CCND1. hsa-mir-98-5p
   interacts with MMP9, CD44 and CCND1. hsa-mir-98-5p interacts with MMP9,
   CD44 and CCND1.

Figure 8.

   [118]Figure 8
   [119]Open in a new tab

   miRNA-gene network of 9 hub genes, red circles represent hub genes,
   yellow diamonds represent miRNAs.

Table 3.

   The microRNA of identified hub genes.
   Identified genes microRNA
   CCND1 hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7d-5p,
   hsa-let-7e-5p, hsa-let-7f-5p, hsa-mir-15a-5p, hsa-mir-16-5p,
   hsa-mir-17-5p, hsa-mir-18a-5p, hsa-mir-19a-3p, hsa-mir-20a-5p,
   hsa-mir-24-3p, hsa-mir-26b-5p, hsa-mir-27a-3p, hsa-mir-29a-3p,
   hsa-mir-92a-3p, hsa-mir-93-5p, hsa-mir-95-3p, hsa-mir-96-5p,
   hsa-mir-98-5p , hsa-mir-101-3p, hsa-mir-106a-5p, hsa-mir-196a-5p,
   hsa-mir-147a, hsa-mir-34a-5p, hsa-mir-183-5p, hsa-mir-211-5p,
   hsa-mir-224-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-mir-1-3p,
   hsa-mir-15b-5p, hsa-mir-138-5p, hsa-mir-142-5p, hsa-mir-152-3p,
   hsa-mir-9-5p, hsa-mir-146a-5p, hsa-mir-193a-3p, hsa-mir-195-5p,
   hsa-mir-206, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-302a-3p,
   hsa-mir-34c-5p, hsa-mir-365a-3p, hsa-mir-302c-3p, hsa-mir-369-3p,
   hsa-mir-374a-5p, hsa-mir-380-5p, hsa-mir-383-5p, hsa-mir-326,
   hsa-mir-338-3p, hsa-mir-424-5p, hsa-mir-18b-5p, hsa-mir-20b-5p,
   hsa-mir-448, hsa-mir-449a, hsa-mir-490-3p, hsa-mir-202-3p,
   hsa-mir-432-3p, hsa-mir-494-3p, hsa-mir-193b-3p, hsa-mir-497-5p,
   hsa-mir-520a-3p, hsa-mir-526b-3p, hsa-mir-520b, hsa-mir-518c-5p,
   hsa-mir-519d-3p, hsa-mir-516b-5p, hsa-mir-503-5p, hsa-mir-513a-5p,
   hsa-mir-545-3p, hsa-mir-556-5p, hsa-mir-558, hsa-mir-563,
   hsa-mir-593-5p, hsa-mir-603, hsa-mir-608, hsa-mir-625-5p, hsa-mir-646,
   hsa-mir-425-5p, hsa-mir-668-3p, hsa-let-7a-3p, hsa-mir-16-1-3p,
   hsa-mir-19b-1-5p, hsa-mir-21-3p, hsa-mir-34a-3p, hsa-mir-214-5p,
   hsa-mir-223-5p, hsa-mir-145-3p, hsa-mir-34b-3p, hsa-mir-340-5p,
   hsa-mir-330-5p, hsa-mir-342-5p, hsa-mir-323a-5p, hsa-mir-491-3p,
   hsa-mir-574-5p, hsa-mir-576-3p, hsa-mir-593-3p, hsa-mir-298,
   hsa-mir-891b, hsa-mir-892b, hsa-mir-541-5p, hsa-mir-875-3p,
   hsa-mir-876-3p, hsa-mir-708-5p, hsa-mir-374b-5p, hsa-mir-944,
   hsa-mir-1234-3p, hsa-mir-1271-5p, hsa-mir-548k, hsa-mir-1293,
   hsa-mir-1299, hsa-mir-1243, hsa-mir-1253, hsa-mir-1257, hsa-mir-1276,
   hsa-mir-1915-5p, hsa-mir-2861, hsa-mir-3126-5p, hsa-mir-3135a,
   hsa-mir-466, hsa-mir-3147, hsa-mir-3148, hsa-mir-3153, hsa-mir-3157-5p,
   hsa-mir-3170, hsa-mir-1193, hsa-mir-323b-3p, hsa-mir-3179,
   hsa-mir-3202, hsa-mir-4311, hsa-mir-4261, hsa-mir-3609, hsa-mir-3648,
   hsa-mir-3663-5p, hsa-mir-3714, hsa-mir-3907, hsa-mir-3919,
   hsa-mir-676-5p, hsa-mir-3941, hsa-mir-4425, hsa-mir-548ah-5p,
   hsa-mir-4458, hsa-mir-4464, hsa-mir-4483, hsa-mir-4500, hsa-mir-2392,
   hsa-mir-4524a-5p, hsa-mir-4533, hsa-mir-548an, hsa-mir-4537,
   hsa-mir-3189-5p, hsa-mir-3922-5p, hsa-mir-3940-5p, hsa-mir-3973,
   hsa-mir-4633-3p, hsa-mir-4643, hsa-mir-4646-5p, hsa-mir-4651,
   hsa-mir-4664-5p, hsa-mir-4666a-5p, hsa-mir-4666a-3p, hsa-mir-4672,
   hsa-mir-4691-5p, hsa-mir-4691-3p, hsa-mir-4694-3p, hsa-mir-4708-5p,
   hsa-mir-4709-3p, hsa-mir-4715-5p, hsa-mir-4716-3p, hsa-mir-4720-3p,
   hsa-mir-4731-5p, hsa-mir-4735-3p, hsa-mir-3591-3p, hsa-mir-4746-3p,
   hsa-mir-4747-5p, hsa-mir-4748, hsa-mir-4756-3p, hsa-mir-4758-3p,
   hsa-mir-4781-3p, hsa-mir-4782-5p, hsa-mir-4789-3p, hsa-mir-4796-3p,
   hsa-mir-4797-5p, hsa-mir-4803, hsa-mir-4804-3p, hsa-mir-1273f,
   hsa-mir-5009-3p, hsa-mir-5010-3p, hsa-mir-5087, hsa-mir-5196-5p,
   hsa-mir-5197-3p, hsa-mir-4524b-5p, hsa-mir-5572, hsa-mir-5587-5p,
   hsa-mir-5589-5p, hsa-mir-5590-3p, hsa-mir-548av-5p, hsa-mir-5692c,
   hsa-mir-5692b, hsa-mir-5706, hsa-mir-204-3p, hsa-mir-3191-5p,
   hsa-mir-758-5p, hsa-mir-6077, hsa-mir-6500-5p, hsa-mir-6500-3p,
   hsa-mir-6511a-5p, hsa-mir-651-3p, hsa-mir-653-3p, hsa-mir-655-5p,
   hsa-mir-216b-3p, hsa-mir-1910-3p, hsa-mir-5699-5p, hsa-mir-6733-5p,
   hsa-mir-6735-3p, hsa-mir-6737-5p, hsa-mir-6739-5p, hsa-mir-6747-5p,
   hsa-mir-6748-3p, hsa-mir-6764-3p, hsa-mir-6768-5p, hsa-mir-6792-3p,
   hsa-mir-6794-5p, hsa-mir-6812-5p, hsa-mir-6818-5p, hsa-mir-6819-5p,
   hsa-mir-6824-3p, hsa-mir-6838-5p, hsa-mir-6843-3p, hsa-mir-6848-3p,
   hsa-mir-6855-5p, hsa-mir-6867-5p, hsa-mir-6873-5p, hsa-mir-6874-5p,
   hsa-mir-6875-5p, hsa-mir-6884-3p, hsa-mir-6885-3p, hsa-mir-7107-5p,
   hsa-mir-7112-3p, hsa-mir-7706, hsa-mir-7845-5p, hsa-mir-8054,
   hsa-mir-8066, hsa-mir-8068, hsa-mir-8069, hsa-mir-8080,
   hsa-mir-1199-3p, hsa-mir-9500
   IL6 hsa-let-7a-5p, hsa-let-7c-5p, hsa-let-7f-5p, hsa-mir-26a-5p,
   hsa-mir-98-5p, hsa-mir-106a-5p, hsa-mir-107, hsa-mir-203a-3p,
   hsa-mir-223-3p, hsa-mir-1-3p, hsa-mir-124-3p, hsa-mir-142-3p,
   hsa-mir-9-5p , hsa-mir-136-5p, hsa-mir-146a-5p, hsa-mir-149-5p,
   hsa-mir-155-5p, hsa-mir-365a-3p, hsa-mir-335-5p, hsa-mir-451a,
   hsa-mir-146b-5p, hsa-mir-125a-3p
   IGF1 hsa-let-7e-5p, hsa-mir-24-3p, hsa-mir-26a-5p, hsa-mir-26b-5p,
   hsa-mir-27a-3p, hsa-mir-28-5p, hsa-mir-29a-3p, hsa-mir-98-5p,
   hsa-mir-199a-3p, hsa-mir-129-5p, hsa-let-7i-5p, hsa-mir-1-3p,
   hsa-mir-128-3p, hsa-mir-133a-3p, hsa-mir-190a-5p, hsa-mir-302a-5p ,
   hsa-mir-299-3p, hsa-mir-130b-3p, hsa-mir-18b-5p, hsa-mir-483-3p,
   hsa-mir-485-5p, hsa-mir-505-3p, hsa-mir-539-5p, hsa-mir-567,
   hsa-mir-576-5p, hsa-mir-603, hsa-mir-421, hsa-mir-297,
   hsa-mir-30c-2-3p, hsa-mir-10a-3p, hsa-mir-214-5p, hsa-mir-223-5p,
   hsa-mir-30b-3p, hsa-mir-149-3p, hsa-mir-30c-1-3p, hsa-mir-130b-5p ,
   hsa-mir-377-5p, hsa-mir-455-3p, hsa-mir-574-5p, hsa-mir-190b,
   hsa-mir-665, hsa-mir-1305, hsa-mir-3122, hsa-mir-3065-5p, hsa-mir-3197,
   hsa-mir-4273, hsa-mir-4284, hsa-mir-3689a-3p, hsa-mir-3689b-3p,
   hsa-mir-3913-5p, hsa-mir-3929, hsa-mir-4459, hsa-mir-4478,
   hsa-mir-3689c, hsa-mir-3689d, hsa-mir-4419b , hsa-mir-4649-3p,
   hsa-mir-4662a-3p, hsa-mir-4711-3p, hsa-mir-4728-5p, hsa-mir-4753-3p,
   hsa-mir-5011-5p, hsa-mir-211-3p, hsa-mir-450a-1-3p, hsa-mir-1277-5p,
   hsa-mir-6086, hsa-mir-6087, hsa-mir-6134, hsa-mir-6499-3p,
   hsa-mir-6513-5p, hsa-mir-6514-3p, hsa-mir-190a-3p, hsa-mir-887-5p ,
   hsa-mir-6739-3p, hsa-mir-6765-5p, hsa-mir-6771-3p, hsa-mir-6779-5p,
   hsa-mir-6780a-5p, hsa-mir-6785-5p, hsa-mir-6788-5p, hsa-mir-6799-5p,
   hsa-mir-6811-3p, hsa-mir-6840-3p, hsa-mir-6847-3p, hsa-mir-6851-5p,
   hsa-mir-6867-5p, hsa-mir-6880-5p, hsa-mir-6883-5p, hsa-mir-6884-5p,
   hsa-mir-7106-5p, hsa-mir-7156-5p, hsa-mir-7159-5p, hsa-mir-7160-5p,
   hsa-mir-7641, hsa-mir-1273h-5p, hsa-mir-6516-5p, hsa-mir-7847-3p,
   hsa-mir-7977
   MMP9 hsa-let-7e-5p, hsa-mir-21-5p, hsa-mir-29b-3p, hsa-mir-204-5p,
   hsa-mir-211-5p, hsa-mir-15b-5p, hsa-mir-132-3p, hsa-mir-143-3p,
   hsa-mir-9-5p, hsa-mir-9-3p, hsa-mir-320a, hsa-mir-302a-5p,
   hsa-mir-338-3p, hsa-mir-133b, hsa-mir-451a, hsa-mir-491-5p,
   hsa-mir-524-5p, hsa-mir-892b, hsa-mir-133a-5p, hsa-mir-942-3p,
   hsa-mir-203a-5p
   CD44 hsa-mir-16-5p, hsa-mir-30a-5p, hsa-mir-199a-5p, hsa-mir-199a-3p,
   hsa-mir-34a-5p, hsa-mir-204-5p, hsa-mir-211-5p, hsa-mir-216a-5p,
   hsa-mir-218-5p, hsa-mir-1-3p, hsa-mir-15b-5p, hsa-mir-125b-5p,
   hsa-mir-143-3p, hsa-mir-145-5p, hsa-mir-188-5p, hsa-mir-320a,
   hsa-mir-302b-5p, hsa-mir-373-3p, hsa-mir-379-5p, hsa-mir-330-3p,
   hsa-mir-328-3p, hsa-mir-520a-3p, hsa-mir-520c-3p, hsa-mir-579-3p,
   hsa-mir-608, hsa-mir-765, hsa-mir-192-3p, hsa-mir-149-3p,
   hsa-mir-130b-5p, hsa-mir-302d-5p, hsa-mir-708-5p, hsa-mir-744-5p,
   hsa-mir-1321, hsa-mir-3132, hsa-mir-3175, hsa-mir-4270,
   hsa-mir-3667-3p, hsa-mir-4441, hsa-mir-3121-5p, hsa-mir-3127-3p,
   hsa-mir-4642, hsa-mir-3529-5p, hsa-mir-4728-5p, hsa-mir-4739,
   hsa-mir-4753-3p, hsa-mir-4756-5p, hsa-mir-4776-3p, hsa-mir-4793-5p,
   hsa-mir-4802-3p, hsa-mir-5196-3p, hsa-mir-4524b-3p, hsa-mir-664b-3p,
   hsa-mir-5696, hsa-mir-197-5p, hsa-mir-6716-5p, hsa-mir-328-5p,
   hsa-mir-552-5p, hsa-mir-942-3p, hsa-mir-6731-5p, hsa-mir-6734-3p,
   hsa-mir-6754-5p, hsa-mir-6755-5p, hsa-mir-6756-3p, hsa-mir-6785-5p,
   hsa-mir-6826-3p, hsa-mir-6832-3p, hsa-mir-6866-3p, hsa-mir-6883-5p,
   hsa-mir-7106-5p, hsa-mir-8085
   FN1 hsa-mir-26b-5p, hsa-mir-218-5p, hsa-mir-200b-3p, hsa-let-7g-5p,
   hsa-mir-1-3p, hsa-mir-200c-3p, hsa-mir-615-3p, hsa-mir-140-3p
   CXCR4 hsa-mir-26b-5p, hsa-mir-139-5p, hsa-mir-204-5p, hsa-mir-224-5p,
   hsa-mir-9-5p, hsa-mir-9-3p, hsa-mir-126-3p, hsa-mir-146a-5p,
   hsa-mir-150-5p, hsa-mir-335-5p, hsa-mir-133b, hsa-mir-494-3p,
   hsa-mir-622, hsa-mir-663a, hsa-mir-146a-3p, hsa-mir-494-5p
   SPP1 hsa-mir-181b-5p, hsa-mir-146a-5p, hsa-mir-335-5p, hsa-mir-299-5p,
   hsa-mir-127-5p, hsa-mir-4262
   BGN hsa-mir-3125, hsa-mir-4311, hsa-mir-3916, hsa-mir-4476,
   hsa-mir-4533, hsa-mir-6758-5p, hsa-mir-6828-5p, hsa-mir-6856-5p,
   hsa-mir-6859-5p, hsa-mir-6876-5p, hsa-mir-8485
   [120]Open in a new tab

Transcription factors and miRNA network construction of hub genes

   To build the network between target miRNA-transcription and factor-hub
   genes, we used the Network Analyst database. Cytoscape software was
   used to visualize and analyze the generated data results ([121]
   Figure 9 ).

Figure 9.

   [122]Figure 9
   [123]Open in a new tab

   Transcription factor-miRNA-gene network of 13 hub genes, red circles
   represent hub genes, blue diamonds represent transcription factors, and
   orange diamonds represent miRNAs.

Discussion

   In practically all developing and developed nations, the prevalence of
   overweight and obesity has substantially increased. In developed
   nations, it has reached prevalence levels of 60–70% of the adult
   population and is particularly prevalent in cities and among women.
   Undoubtedly, this poses a significant threat to the weight of the
   global economy ([124]28). While obesity-related endocrine disturbance
   is significantly linked to the onset of cancer, earlier research has
   found a connection between obesity and CC in males. It is still not
   entirely apparent how CC is related to obese females. Therefore, it is
   very important to investigate any potential biological processes that
   link female obesity to the development of CC. In this study dataset
   [125]GSE44076, female CC patients had an average age of 69.63 ± 9.44
   years. These female patients were diagnosed with CC when they were in
   the menopausal stage. Endocrine disturbance and the chronic
   inflammatory state brought on by weight changes in these patients may
   be directly related to the onset of CC at this period.

   In this study, we downloaded data from two gene chips from the GEO
   website and performed a series of bioinformatic analyses based on obese
   women and women with CC. Between the two datasets, we were able to
   identify 146 DEGs. We discovered that these genes were primarily
   enriched in inflammatory and immune-related pathways by GO analysis and
   KEGG analysis. These pathways were tightly linked to CC development and
   endocrine dysregulation brought on by obesity. We also discovered 14
   hub genes, including TYROBP, CD44, BGN, FCGR3A, CD53, CXCR4, FN1, SPP1,
   IGF1, CCND1, MMP9, IL2RG, IL6 and CTGF, which were crucial in the
   development of CC and obesity. Then, for these 14 hub genes, we created
   the miRNAs-gene-transcription factor regulatory networks.

   Previous research has demonstrated that obesity and overweight also
   contribute to cancer. 20% of cancer deaths among women in the US are
   brought on by being overweight or obese ([126]6). Obesity commonly
   causes metabolic problems, altered steroid hormone secretion and
   persistent subclinical inflammation, all of which are highly related
   with the development of cancer ([127]29). Circulating estrogen levels
   in postmenopausal women are inversely correlated with fat mass. The
   primary source of the synthesis of circulating estrogen comes from
   androgens in adipose tissue. It has been demonstrated that greater
   estrogen levels are linked to obesity and overweight ([128]30).
   Increased amounts of circulating estrogen alter the levels of
   cytokines, hormones and inflammatory markers, which may worsen the
   prognosis for cancer patients ([129]31). Immune cells, including
   macrophages and lymphocytes, penetrate white adipose tissue in obese
   people. Obese fat pads resemble chronically injured tissue in this
   sense, and they can be a rich source of pro-inflammatory chemicals that
   may promote tumor growth ([130]32). The adipose microenvironment in the
   obese population is comparable to the tumor microenvironment in many
   ways, including cellular composition, chronic low-grade inflammation
   and a high ratio of reactive oxygen species to antioxidants ([131]33).
   These possible mechanisms are similar to the gene functions that we
   discovered to be frequently dysregulated in both datasets. Our findings
   also indicate that the overlapped gene functions in the two datasets
   are predominantly enriched in inflammatory and immune-related pathways.
   To summarize, obesity and cancer formation are inextricably linked
   because obesity’s endocrine disruption and chronic inflammatory state
   provide a potential material basis for tumor development. In the case
   of women, we must consider the unique circumstance of the patient’s
   menopausal status, since this condition is also linked to the
   development of obesity.

   The NetworkAnalyst program was used to generate these hub
   gene-associated target miRNAs and transcription factors. The four most
   active miRNAs with the hub genes were miR-1-3p, miR-26b-5p,
   miR-164a-5p, and miR-9-5p. WRNIP1, ATF1, CBFB, and NR2F6 were the four
   transcription factors that interacted the most with the hub genes.
   MiRNAs serve important roles in cellular processes such as cellular
   differentiation, stress response, proliferation, and apoptosis.
   Abnormal miRNA expression is linked to carcinogenesis and cancer
   progression ([132]34). MiRNAs are intriguing molecular biomarkers for
   cancer diagnosis, prognosis, and responsive therapeutic targets,
   including colon cancer ([133]35). Transcription factors can be found in
   a variety of human organs and cells. To control transcription,
   recognize certain DNA sequences ([134]36). We expected that the
   transcription factors WRNIP1, ATF1, CBFB, and NR2F6, as well as the
   microRNAs miR-1-3p, miR-26b-5p, miR-164a-5p, and miR-9-5p, would play
   essential roles in metabolic abnormalities of cells in obese people and
   the development of colon cancer.

   However, our research is bound to have limitations. We were unable to
   identify a dataset on CC in the setting of female obesity to support
   our findings. In addition, we lack matching transcriptome sequencing
   data to back up our findings. Future research should look for
   equivalent validation datasets to validate the findings. As a result,
   subsequent pertinent experimental studies reveal possible diagnostic
   and therapeutic targets for female obesity-related CC patients.

   In conclusion, our research discovered a number of critical genes.
   miRNAs - gene-transcription factor regulatory networks - play a role in
   the pathophysiology of female obesity and female CC, making them
   attractive diagnostic and therapeutic targets for CC in the setting of
   female obesity.

Conclusion

   Our findings show that females with obesity and females with CC have
   shared genes. Our findings suggest that obesity and CC can be
   genetically connected in female individuals. Between female adipose
   patients and female CC, we discovered 14 hub genes and built a
   transcription factor-miRNA-gene network comprising hub genes. WRNIP1,
   ATF1, CBFB, and NR2F6 transcription factors were found in our study.
   Furthermore, hsa-mir-1-3p, hsa-mir-26b-5p, hsa-mir-164a-5p, and
   hsa-mir-9-5p are implicated in the development of female obesity and
   female colon cancer via inflammatory and immune-related pathways. Our
   findings could help with future mechanistic research and medication
   target prediction.

Data availability statement

   The datasets presented in this study can be found in online
   repositories. The names of the repository/repositories and accession
   number(s) can be found in the article/[135] Supplementary Material .

Ethics statement

   Ethical approval was not required for the study involving humans in
   accordance with the local legislation and institutional requirements.
   Written informed consent to participate in this study was not required
   from the participants or the participants’ legal guardians/next of kin
   in accordance with the national legislation and the institutional
   requirements. Written informed consent was not obtained from the
   individual(s) for the publication of any potentially identifiable
   images or data included in this article because Our raw data is
   obtained from a public database.

Author contributions

   X-LZ, H-YL and TY conceived and designed the study. YF, X-FZ, M-LL, RS
   and YG collected and analyzed the data. X-LZ wrote the original draft.
   H-YL and YT revised the manuscript. X-LZ, H-YL and YT reviewed and
   edited the manuscript. All authors contributed to the article and
   approved the submitted version. All authors contributed to the article
   and approved the submitted version.

Funding Statement

   This work was supported by the National Natural Science Foundation of
   China (82060525).

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary Material for this article can be found online at:
   [136]https://www.frontiersin.org/articles/10.3389/fendo.2023.1189570/fu
   ll#supplementary-material
   [137]Click here for additional data file.^ (630.3KB, tif)
   [138]Click here for additional data file.^ (603.6KB, tif)
   [139]Click here for additional data file.^ (290.5KB, tif)
   [140]Click here for additional data file.^ (342.6KB, tif)
   [141]Click here for additional data file.^ (660.8KB, tif)
   [142]Click here for additional data file.^ (115KB, xlsx)
   [143]Click here for additional data file.^ (14.6KB, xlsx)

References