Abstract
Aim
The reasons behind the high prevalence of poorly differentiated gastric cancer in young females remain unclear. Therefore, this study aimed to conduct a comprehensive genetic analysis to investigate the factors responsible for the high prevalence of poorly differentiated gastric cancer in young females.
Methods
We analyzed 299 patients who underwent gastric cancer surgery at the Gunma University Hospital between April 2015 and December 2020. Among them, we selected cases of poorly differentiated gastric cancer in females, differentiated gastric cancer in females, and poorly differentiated gastric cancer in males, aged 30–50 years. Three eligible cases of each condition were found and included in the study. RNA was isolated from dissected formalin‐fixed, paraffin‐embedded tissue samples, followed by RNA sequencing. The results were analyzed using ingenuity pathway analysis to elucidate the mechanisms contributing to the high incidence of poorly differentiated gastric cancer in young females.
Results
Dexamethasone, β‐estradiol, and interleukin‐1β were identified as significant upstream regulators associated with poorly differentiated gastric cancer in young females. The downstream target genes of β‐estradiol included male germ cell‐associated kinase, growth differentiation factor 6, endothelin 2, and collagen type XI alpha 1 chain.
Conclusion
Our detailed RNA‐seq analysis revealed that the female sex hormone, β‐estradiol, plays a role in the development of poorly differentiated gastric cancer in young females.
Keywords: estradiol, gastric cancer, growth differentiation factor 6, interleukin‐1β, RNA‐seq
Although the involvement of female sex hormones in the development of poorly differentiated gastric cancer in young females is controversial, our detailed analysis using RNA‐seq suggested that the female sex hormone, β‐estradiol plays a role in the development of poorly differentiated gastric cancer in young females.
1. INTRODUCTION
Gastric cancer (GC) ranks as the fifth most prevalent cancer worldwide and the fourth leading cause of cancer‐related mortality. 1 Its incidence is particularly high in East Asia and South America. 2 GC is a multifactorial disease, with its development influenced by a combination of environmental and genetic factors. 3 Most GC cases are sporadic and etiologically related to Helicobacter pylori infection. H. pylori accounts for approximately 89% of GC cases. 4 The global incidence of GC has been steadily declining, primarily because of reduced rates of H. pylori infection, smoking, and the availability of fresh fruits and vegetables. 5 However, according to the Lauren classification, the proportion of diffuse‐type GC is increasing. 6 , 7 Additionally, the incidence of signet ring cell carcinoma (SRC) has been increasing in the United States of America. 8 Although GC is more common in males, poorly differentiated GC is characterized by a higher incidence in females and is more common in the younger age groups. 9 We have previously reported that poorly cohesive carcinoma (PCC), which comprises poorly differentiated non‐solid‐type adenocarcinoma and SRC, is observed more frequently in young females. Moreover, PCC is associated with decreased expression of epithelial‐mesenchymal transition (EMT) markers, such as E‐cadherin and the activation of Wnt3a signaling. 10
Subsequently, we aimed to understand why poorly differentiated GC is frequently observed in young females. Some studies have suggested an association between female hormones and the risk of GC; however, no clear relationship has been elucidated. 11 , 12 , 13 Therefore, in this study, we aimed to conduct a comprehensive genetic analysis to investigate the factors responsible for the high prevalence of poorly differentiated GC in young females.
2. METHODS
2.1. Patients
A total of 299 patients who underwent GC surgery between April 2015 and December 2020 were included in this study. We classified tumors with por2 or sig histology as poorly differentiated carcinomas and those with tub1 or tub2 as differentiated carcinomas. Cases treated preoperatively or via endoscopic submucosal dissection were excluded from the analysis. Among these, we selected cases of female poorly differentiated GC, female differentiated GC, and male poorly differentiated GC, aged 30–50 years. There were three eligible cases for each condition, all of which were included. This study was approved by the Institutional Review Board of Gunma University (Approval no. HS2021‐174, HS2022‐153) and was conducted according to the guidelines put forth by the Declaration of Helsinki. This was a retrospective study; therefore, the IRB of Gunma University waived the requirement to obtain informed consent. An opt‐out method was assigned to obtain the participant's consent.
2.2. RNA isolation and RNA‐seq analysis
Total RNA was extracted from macroscopically dissected formalin‐fixed and paraffin‐embedded tissue samples using a High Pure FFPET RNA Isolation Kit (Roche, Basel, Switzerland), according to the manufacturer's protocol. Next, RNA was quantified using a NanoVue Plus (Fisher Scientific, Leicestershire, United Kingdom). The DV200 was measured using an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), and a reagent from the Agilent RNA 6000 Pico kit (Agilent Technologies) was used to confirm that the RNA was of good quality (DV200 >50%). Total RNA (150 ng) was used to generate sequencing libraries with a QIAseq FastSelect‐rRNA HMR Kit (QIAGEN, Venlo, Netherlands) and a KAPA RNA HyperPrep Kit (KAPA Biosystems, MA, USA), following the manufacturer's instructions. A Qubit dsDNA HS Assay Kit (Invitrogen, MA, USA) was used for the quantitative analysis of library RNA. An Agilent High Sensitivity DNA kit (Agilent, CA, USA) was used for quality evaluation. RNA sequencing was performed using a NextSeq 500/550 High Output Kit v2.5 (38 bp paired‐end reads) (Illumina, CA, USA) and an Illumina NextSeq 500 (Illumina, CA, USA), according to the corresponding manufacturers' protocols. Illumina NextSeq 500 was used for analysis. Fastp (v0.21.0) was used for sequence‐read qualification and trimming before alignment. The reads were then aligned to the UCSC reference human genome 19 using spliced transcript alignment to a reference (STAR) software v2.5.3a (DNASTAR, WI, USA). 14 Reads were counted using RSEM (v1.3.3). 15 The number of input reads was greater than 46 million for all samples. The median number of uniquely mapped reads was approximately 44 million (92.2%). Differentially expressed genes (DEGs) were detected using the TCC ‐iDEGES ‐ edgeR pipeline in R (https://www.R‐project.org/, version 4.0.3) package TCC (version 1.30.0). 16 The RNA‐seq results were analyzed using ingenuity pathway analysis (IPA, QIAGEN; content version 94 302 991 (Release date: May 27, 2023)).
2.3. Assessment utilizing public databases
We utilized the Kaplan–Meier Plotter (https://kmplot.com/analysis/) for prognostic analysis and cBioPortal (https://www.cbioportal.org/) for data analysis using TCGA (PanCancer Atlas). 17 In the Kaplan–Meier Plotter, gene expression and GC prognosis were analyzed based on the Lauren classification and gender differences in surgical cases. The cutoff value used was “auto‐select best cutoff.”
3. RESULTS
3.1. Clinicopathological features of the included patients
The clinicopathological features of patients are shown in Table 1. Case 4 was excluded from the RNA‐seq analysis because of keratin contamination. Therefore, the study included three cases each of poorly differentiated GC in young females and males along with two cases of differentiated GC in young females. Patient comparisons are shown in Figure 1. DEGs were extracted at p < 0.05. A total of 244 genes, as shown in Figure 1, were extracted and analyzed as characteristic genes of poorly differentiated GC in young females.
TABLE 1.
Clinicopathological features of the patients included in this study.
| Cases | Age | Sex | Localization | Type | Lauren classificasion | Diameter (mm) | Depth of invasion | pN | Cytology | Operation |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 41 | F | UM | 3 | Diffuse | 92 | SE | 1 | 0 | TG |
| 2 | 35 | F | MU | IIb | Diffuse | 190 | M | 0 | 0 | TG |
| 3 | 43 | F | ML | 4 | Diffuse | 123 | SE | 3b | 1 | TG |
| 4 | 47 | F | UM | IIc | Intestinal | 10 | M | 0 | 0 | PG |
| 5 | 43 | F | L | IIc | Intestinal | 41 | MP | 0 | 0 | DG |
| 6 | 33 | F | M | IIc | Intestinal | 35 | SM | 1 | 0 | DG |
| 7 | 41 | M | M | IIc | Diffuse | 14 | M | 0 | 0 | DG |
| 8 | 44 | M | L | IIc | Diffuse | 15 | M | 0 | 0 | DG |
| 9 | 48 | M | M | IIc | Diffuse | 44 | SM | 1 | 0 | TG |
Abbreviations: DG, distal gastrectomy; F, female; L, lower part; M, male; M, middle part; M, mucosa; MP, tunica muscularis propria; PG, proximal gastrectomy; SE, serosa; SM, submucosa; TG, total gastrectomy; U, Upper part.
FIGURE 1.
Extraction of characteristic genes of poorly differentiated GC in young females. DEGs, differentially expressed genes; GC, gastric cancer.
3.2. Analysis of upstream regulators
Upon analyzing the upstream regulators (Table 2) of poorly differentiated GC in young females, the following were identified as significant: dexamethasone (p‐value = 2.00E‐14), β‐estradiol (p‐value = 2.40E‐12), and interleukin 1β (p‐value = 5.53E‐12). The female hormone β‐estradiol was also extracted.
TABLE 2.
Results of the analysis of upstream regulators of poorly differentiated GC in young females.
| Name | p‐value |
|---|---|
| dexamethasone | 2.00E‐14 |
| β‐estradiol | 2.40E‐12 |
| interleukin 1β | 5.53E‐12 |
3.3. Analysis of downstream genes of β‐estradiol
In this study, we focused on β‐estradiol and analyzed its downstream genes. Table 3 shows the results of the study. The following downstream genes of β‐estradiol were extracted: male germ cell‐associated kinase (MAK; expression log ratio = 12.2), growth differentiation factor 6 (GDF6; expression log ratio = 11.6), endothelin 2 (EDN2; expression log ratio = 10.8), collagen type XI alpha 1 chain (COL11A1; expression log ratio = 3.7), early growth response 2 (EGR2; expression log ratio = 3.5), hemoglobin subunit beta (HBB; expression log ratio = 3.1), and thrombospondin 4 (THBS4; expression log ratio = 2.6).
TABLE 3.
Expression of downstream genes of β‐estradiol in young females with poorly differentiated GC.
| Genes | Expr log ratio |
|---|---|
| MAK | 12.2 |
| GDF6 | 11.6 |
| EDN2 | 10.8 |
| COL11A1 | 3.7 |
| EGR2 | 3.5 |
| HBB | 3.1 |
| THBS4 | 2.6 |
Abbreviations: COL11A1, collagen type XI alpha 1 chain; EDN2, endothelin 2; EGR2, early growth response 2; GDF6, growth differentiation factor 6; HBB, hemoglobin subunit beta; MAK, male germ cell‐associated kinase; THBS4; thrombospondin 4.
3.4. Analysis of the top canonical pathways
The analysis results of the top canonical pathways are summarized in Table S1. The top canonical pathways detected were as follows: dilated cardiomyopathy signaling pathway (p‐value = 1.07E‐05), hepatic fibrosis/ hepatic stellate cell activation (p‐value = 1.32E‐05), collagen degradation (p‐value = 2.04E‐05), collagen chain trimerization (p‐value = 5.01E‐05), and granulocyte adhesion and diapedesis (p‐value = 6.61E‐05).
3.5. Prognostic analysis using the Kaplan–Meier plotter of MAK , EDN2 , COL11A1 , and EGR2 , which are downstream genes regulated by β‐estradiol
A prognostic analysis was conducted on MAK, EDN2, COL11A1, and EGR2, which were identified as downstream genes regulated by β‐estradiol, as shown in Table 3. The expression levels of MAK and EDN2 did not impact prognosis in Figure S1. However, the expression of COL11A1 (p = 0.027) and EGR2 (p = 0.008) was linked to a poor prognosis, specifically in females with poorly differentiated GC.
3.6. Estrogen receptor alpha ( ESR1 ) expression and prognostic analysis
The expression level of ESR1, the receptor for β‐estradiol, was analyzed using cBioPortal, as shown in Figure S2. ESR1 expression did not correlate with age in male GC patients (p = 0.57); however, in females, ESR1 expression tended to be higher in patients under 50 years of age (p = 0.068). A prognostic analysis was also conducted on ESR1, as shown in Figure S3; however, there was no significant difference in diffuse‐type GC based on sex.
4. DISCUSSION
In this study, we show that dexamethasone, β‐estradiol, and interleukin 1β were predicted as significant upstream regulators of poorly differentiated GC in young females. Furthermore, the downstream genes regulated by β‐estradiol, MAK, GDF6, EDN2, and COL11A1 were extracted. These results suggest that β‐estradiol was involved in poorly differentiated GC in young females.
Sex differences in poorly differentiated GC have not been fully elucidated. Estrogens, including estrone, estriol, and the metabolite 17β‐estradiol, have been reported to play pathophysiological roles in gastroesophageal reflux disease, esophageal cancer, peptic ulcer disease, and GC. 18 Furthermore, the relationship between estrogen, a female hormone, and GC has been previously reported. In a study relating estrogen and GC risk, no association was found between age at menarche, number of children, breastfeeding status, or oral contraceptive use. 19 There are reports of a lower risk of GC in females with late menopause and in females receiving hormone replacement therapy than in untreated females, indicating a possible protective effect of estrogen against GC development. 19 , 20 , 21 , 22 , 23 In an insulin‐gastrin male mouse model infected with H. pylori, 17β‐estradiol prevented H. pylori‐induced GC by reducing leukocyte recruitment and downregulating oncogenic signaling pathways. 24 Kuo et al. reported that treatment with 17β‐estradiol significantly suppressed human bone marrow mesenchymal stem cell‐induced motility in human AGS GC cells. 25 Conversely, estrogen has been reported to rapidly aggravate diffuse‐type GC in young females. Kang et al. reported that estradiol promotes EMT and stemness phenotypes in MKN45 cells. They described rapid diffuse‐type GC progression in young females with physiologically high estrogen levels and suggested that fulvestrant treatment with ovarian suppression may serve as a tumor suppressor in premenopausal patients with diffuse‐type GC. 13 Furthermore, there are reports suggesting that estradiol is associated with peritoneal dissemination and poorly differentiated GC through the downregulation of E‐cadherin expression via ESR1. 13 , 26 , 27 However, another study reported no correlation between estradiol and the risk of GC in males. 28 As shown in Figure S2, ESR1 expression tended to be more prevalent in poorly differentiated GC in females under 50 years of age. In this study, we identified estradiol as an upstream regulator of poorly differentiated GC in young females. This may be linked to the development and progression of poorly differentiated GC, as estradiol impacts ESR1, which is often expressed in poorly differentiated GC among young females. Dexamethasone was also identified as an upstream regulator in this study, and its receptor, glucocorticoid receptor (GR), is expressed in GC. 29 GR has also been associated with primary GC, rather than distant metastasis. 30 In our study, GR expression was linked to a significantly worse prognosis in female patients with diffuse‐type GC, as shown in Figure S4, suggesting that dexamethasone may play a role in the development of diffuse‐type GC in females. As described above, the involvement of female hormones, such as estrogen, in the progression of poorly differentiated GC in young females is controversial. However, in this study, we focused on the relationship between sex differences in young females and GC using RNA sequencing analysis. The results showed that β‐estradiol is an upstream regulator of poorly differentiated GC in young females and contributes to the development of GC. These results support the idea that β‐estradiol is involved in the development of poorly differentiated GC in young females.
In breast cancer, estradiol increases the expression of 6‐phosphofructo‐2‐kinase/fructose‐2,6‐biphosphatase 3 (PFKFB3) and promotes glucose uptake into cancer cells, contributing to cancer cell survival. 31 Furthermore, in ovarian cancer, estradiol has been reported to activate the pyruvate pathway through PFKFB3 expression, induce COL11A1 expression, and is associated with cancer stemness, proliferation, and chemoresistance. 32 Our study also showed that the downstream genes of β‐estradiol were extracted from COL11A1. Furthermore, analysis of the top canonical pathways revealed that COL11A1 expression increased in several ways. We also revealed that COL11A1 is associated with β‐estradiol in the pathway analysis, as shown in Figure 2. COL11A1 has been reported to play a role as a biomarker, prognostic marker, and cancer‐associated fibroblast marker. It is also an important cancer driver, regulating tumor growth, invasion, migration, metastasis, cancer stemness, regulation of extracellular matrix dynamics, and crosstalk with tumor cells. COL11A1 is a marker of poor prognosis marker in GC. 33 An in vitro study using the GC cell line HGC‐27 suggested that COL11A1 contributes to increased proliferation, differentiation, migration, and invasion. 34 In this study, we showed that β‐estradiol induced COL11A1 expression and related pathways through RNA‐seq analysis. Here, we also report, for the first time, that β‐estradiol may be related to poorly differentiated GC. Aside from β‐estradiol, dexamethasone was also detected as an upstream regulator. Dexamethasone inhibits apoptosis in GC cells. 35
FIGURE 2.
Pathway analysis regarding the relationship between COL11A1 with β‐estradiol. COL11A1, collagen type XI alpha 1 chain.
Using RNA‐seq analysis, we demonstrated that β‐estradiol is involved in poorly differentiated GC in young females. In treating poorly differentiated GC in young females, the possibility of using aromatase inhibitors, which are commonly used to treat breast cancer, is expected to be investigated in the future. Aromatase inhibitors have been reported to inhibit GC progression. 36 , 37 This study indicates that aromatase inhibitors could be more effective for treating poorly differentiated GC in young females, and further research in this area is necessary. The findings also suggest that exploring the role of female sex hormones may help in developing more personalized medicine options.
This study has some limitations. Herein, the genes and pathways characteristic of poorly differentiated GC in young females were identified using RNA‐seq. The strength of our study is that we were able to perform RNA‐seq focusing on juvenile GC and its sex differences. The current study was a single‐center, retrospective analysis, and the number of cases was insufficient to account for variability among them. Therefore, publicly available data were used for the analysis; however, the detailed mechanisms of carcinogenesis and its contribution to GC have not been investigated. Therefore, further in vitro and in vivo studies are warranted. In our study, we compared poorly differentiated and differentiated GC in young females and identified β‐estradiol as an upstream regulator of poorly differentiated GC in young females. However, we did not compare these findings with poorly differentiated GC in older patients, nor did we explore the influence of age and female sex hormones. Future studies should focus on examining the characteristics of poorly differentiated GC in both young and older female patients.
In conclusion, although the involvement of female sex hormones in the development of poorly differentiated GC in young females is controversial, our detailed analysis using RNA‐seq suggested that the female sex hormone, β‐estradiol plays a role in the development of poorly differentiated GC in young females.
AUTHOR CONTRIBUTIONS
Nobuhiro Nakazawa: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; validation; visualization; writing – original draft. Takehiko Yokobori: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; supervision; validation; visualization; writing – original draft; writing – review and editing. Yohei Morishita: Data curation; formal analysis; investigation; methodology; resources; validation; visualization; writing – original draft. Akinobu Echigo: Conceptualization; data curation; formal analysis; investigation; methodology; resources; software; validation; visualization; writing – original draft; writing – review and editing. Reika Kawabata‐Iwakawa: Conceptualization; data curation; formal analysis; investigation; methodology; resources; software; validation; visualization; writing – original draft; writing – review and editing. Akiharu Kimura: Data curation; resources; writing – review and editing. Akihiko Sano: Data curation; methodology; resources; writing – review and editing. Makoto Sakai: Data curation; investigation; resources; writing – review and editing. Ken Shirabe: Conceptualization; funding acquisition; methodology; project administration; resources; supervision; writing – review and editing. Hiroshi Saeki: Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; resources; supervision; writing – original draft; writing – review and editing.
FUNDING INFORMATION
This work was supported by JSPS KAKENHI (grant number 23 K15513).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest for this article. K. Shirabe, and H. Saeki are editorial members of Annals of Gastroenterological Surgery.
ETHICS STATEMENT
Approval of the research protocol by an Institutional Reviewer Board: This study was approved by the Institutional Review Board of Gunma University (Approval no. HS2021‐174, HS2022‐153).
Informed Consent: N/A.
Registry and the Registration No. of the study/trial: N/A.
Animal Studies: N/A.
Supporting information
Figure S1. Prognostic analysis using the Kaplan–Meier Plotter of MAK, EDN2, COL11A1, and EGR2, which are downstream genes regulated by β‐estradiol. MAK, male germ cell‐associated kinase; EDN2, endothelin 2; COL11A1, collagen type XI alpha 1 chain; EGR2, early growth response 2.
Figure S2. Estrogen receptor alpha (ESR1) expression.
Figure S3. Prognostic analysis using the Kaplan–Meier Plotter of ESR1. ESR1, Estrogen receptor alpha.
Figure S4. Prognostic analysis using the Kaplan–Meier Plotter of GR. GR, glucocorticoid receptor.
ACKNOWLEDGMENTS
The authors would like to thank Ms. Yukiko Suto, Ms. Yoko Yokoyama, and Ms. Hiroko Matsuda (Education and Research Support Center at Gunma University) for their technical support with RNA‐seq.
Nakazawa N, Yokobori T, Morishita Y, Echigo A, Kawabata‐Iwakawa R, Kimura A, et al. Comprehensive genetic analysis of poorly differentiated gastric cancer in young females. Ann Gastroenterol Surg. 2025;9:926–932. 10.1002/ags3.70020
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are not publicly available as they contain information that could compromise the privacy of the research participants but are available from the corresponding author (T. Y.) upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1. Prognostic analysis using the Kaplan–Meier Plotter of MAK, EDN2, COL11A1, and EGR2, which are downstream genes regulated by β‐estradiol. MAK, male germ cell‐associated kinase; EDN2, endothelin 2; COL11A1, collagen type XI alpha 1 chain; EGR2, early growth response 2.
Figure S2. Estrogen receptor alpha (ESR1) expression.
Figure S3. Prognostic analysis using the Kaplan–Meier Plotter of ESR1. ESR1, Estrogen receptor alpha.
Figure S4. Prognostic analysis using the Kaplan–Meier Plotter of GR. GR, glucocorticoid receptor.
Data Availability Statement
The data that support the findings of this study are not publicly available as they contain information that could compromise the privacy of the research participants but are available from the corresponding author (T. Y.) upon reasonable request.