Home Life Sciences Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
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Small breast epithelial mucin as a useful prognostic marker for breast cancer patients

  • Hui Hao EMAIL logo , Lin Yang , Bingsheng Wang , Yinzhou Sang and Xueliang Liu
Published/Copyright: November 23, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

This study aimed to evaluate the clinical utility of small breast epithelial mucin (SBEM) as a prognostic biomarker in an independent patient cohort. The paraffin-embedded tissues and clinicopathological data of 105 patients with breast cancer were collected, and the expression of SBEM in breast cancer samples was detected by immunohistochemical staining. The correlations between clinicopathological variables and the expression of SBEM were analyzed, and its significance as a prognostic indicator for breast cancer patients was determined. Immunohistochemical staining revealed that SBEM was expressed mostly in the cytomembrane and cytoplasm, with markedly increased SBEM expression (≥4 points on staining intensity) observed in 34 of 105 breast cancer tissues (32.4%). Elevated expression of SBEM was found to be significantly associated with larger tumor size (P = 0.002), more frequent lymph node metastasis (P = 0.029), advanced tumor node metastasis stage (P = 0.005), reduced expression of the progesterone receptor (PR) (P = 0.002), and a higher Ki-67 index (P = 0.006). Survival analysis indicated that patients with elevated SBEM expression had worse overall survival (OS) (5-year OS rate: 50.5 vs 93.9% for high and low SBEM expression, respectively, P < 0.001) and disease-free survival (DFS) (5-year DFS rate: 52.8 vs 81.7% for high and low SBEM expression, respectively, P = 0.001) rates than those with low expression of SBEM. Univariate and multivariate Cox analyses demonstrated that elevated expression of SBEM (hazard ratio [HR] = 1.994, 95% confidence interval [CI]: 1.008–3.945, P = 0.047), tumor size (HR = 2.318, 95% CI: 1.071–5.017, P = 0.033), and PR status (HR = 0.195, 95% CI: 0.055–0.694, P = 0.012) were independent predictors of OS in breast cancer patients. Elevated expression of SBEM was associated with both aggressive tumor characteristics and poor survival, indicating its potential as a useful prognostic biomarker for breast cancer patients.

Keywords: breast cancer; small breast epithelial mucin; immunohistochemistry; prognostic; biomarker

1 Introduction

Breast cancer is a common malignancy and the leading cause of cancer-related death in women. The disease is heterogeneous in terms of molecular features, morphology, and biological behavior [1,2,3]. Due to the lack of recognized symptoms and signs, breast cancer usually presents at an advanced stage, resulting in poor therapeutic efficacy and low survival outcomes [4,5,6]. Therefore, it is of great significance to identify novel prognostic biomarkers and therapeutic targets for breast cancer patients.

The mucin family are large proteins with heavy glycosylation, and it could be classified as membrane-bound (MUC1, MUC4, MUC13, and MUC16) and secretory types (MUC2, MUC5AC, MUC5B, and MUC6). The mucins form a chemical barrier in luminal surfaces of organs such as breast, pancreas, and gastrointestinal tract against infection and inflammation. As essential components of cells, mucin proteins also play important roles in cellular apoptosis, adhesion, and metastasis. It has been reported that mucins are used as specific diagnostic markers and therapeutic targets for human cancers. Small breast epithelial mucin (SBEM), also known as MUCL1, is a key member of the membrane-bound mucin family. SBEM is specifically expressed in salivary and mammary glands [7,8]. A previous study suggested that SBEM may be a valuable biomarker for bone marrow micrometastases in breast cancer patients [9,10]. Emerging evidence has also shown that SBEM levels are markedly increased in the peripheral blood of breast cancer patients in comparison with healthy controls [11]. By contrast, low levels of SBEM were more frequently found in breast cancer patients who underwent neoadjuvant chemotherapy [11]. In addition, SBEM was identified as an oncogene that promoted the migration and invasion of breast cancer cells by the regulation of the epithelial-to-mesenchymal transition [12]. However, the clinical value of SBEM as a prognostic biomarker has not yet been clarified. Therefore, the current study focused on the expression of SBEM in breast cancer tissues using immunohistochemistry and analyzed the correlations between its expression and clinicopathological characteristics as well as survival outcomes in breast cancer patients. Our findings may provide a valuable biomarker for the prognostic assessment and clinical treatment of breast cancer patients.

2 Materials and methods

2.1 Patients and clinical samples

A total of 105 patients who had been diagnosed and treated with surgical resection for breast cancer in our hospital between January 2016 and December 2018 were consecutively enrolled. All cases were pathologically diagnosed with invasive breast carcinoma, and the patients were eligible if they had no evidence of distant metastasis at the initial diagnosis. None of the patients received neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy before surgery. Patients with comorbid malignancies or incomplete clinicopathological and follow-up data were excluded. This study was approved by the Ethics Committee of our institution (No. K2020-003), and informed consent was obtained from all participants before surgery.

Paraffin-embedded tissues and clinicopathological data of 105 breast cancer patients were retrospectively collected. The demographic and clinicopathological features, including patient age, histological grade, tumor size, lymph node metastasis, anatomical tumor node metastasis (TNM) stage, expression of the estrogen receptor (ER) and progesterone receptor (PR), amplification status of human epidermal growth factor receptor 2 (HER2), Ki-67 expression, P53 mutation, and molecular subtype, were analyzed. The expression of ER, PR, and Ki-67 was detected by immunohistochemistry. The threshold of ER and PR positivity was defined as >10%, and cases showing nuclear staining of ≥14% of Ki-67 were classified as having high Ki-67 expression.

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies, and in accordance with the tenets of the Helsinki Declaration, and has been approved by the Ethics Committee of Cangzhou People’s Hospital (No. K2020-003).

2.2 Immunohistochemical analysis

Immunohistochemical staining for SBEM expression was performed using the streptavidin–peroxidase method according to both previous reports and manufacturers’ instructions.

Briefly, fixed tissue samples were embedded in paraffin and cut into 4 µm sections. The sections were then deparaffinized with a xylene solution and rehydrated in descending concentrations of ethanol. Antigen repair and retrieval were conducted with 0.01 M sodium citrate buffer (pH = 6.0) under heating for 30 min at 95°C in a microwave, after which the slides were blocked with 0.3% H2O2 solution for 10 min to inhibit the activity of endogenous peroxidase. After washing with phosphate-buffered saline, the sections were incubated with the primary antibody, polyclonal rabbit anti-SBEM (1:200, ab122530, Abcam, Cambridge, UK), overnight at 4°C. On the following day, the slides were incubated with the secondary antibody (anti-rabbit IgG) for 60 min at room temperature. Immunohistochemical responses were visualized by incubation with diaminobenzidine solution, and the sections were counterstained with hematoxylin. Finally, the slides were dehydrated in an alcohol gradient and fixed with xylene, followed by evaluation and imaging under a microscope.

The expression level of SBEM in breast cancer tissues was semi-quantitatively evaluated according to the intensity of immunohistochemical staining and the percentage of SBEM-positive cells. The staining intensity of SBEM was classified into four grades, namely, negative (= 0 points), weak (= 1 point), moderate (= 2 points), and strong (= 3 points). Five different visual fields were randomly selected, and 200 cells in each were counted. The staining proportions were scored as <5% (= 0 points), 5‒25% (= 1 point), 26‒50% (= 2 points), 51‒75% (= 3 points), and >75% (= 4 points). The immunohistochemical score for SBEM expression = staining intensity * the proportion of positive cells. <4 points was defined as negative SBEM expression, and ≥4 points was defined as positive expression of SBEM.

2.3 Statistical analysis

The patients were divided into negative- and positive-expression groups based on the SBEM immunohistochemical results. Correlations between clinicopathological variables and SBEM expression were summarized using a contingency table and analyzed by Pearson’s chi-squared or Fisher’s exact test. The primary outcomes for the survival analysis were overall survival (OS) and disease-free survival (DFS). The former was defined as the time from the date of surgical treatment to the date of death due to any cause or the last follow-up, while the latter was defined as the time interval from the date of surgical treatment to the date of the first postoperative tumor recurrence or metastasis. Survival differences between patients with negative and positive SBEM expression were evaluated by the Kaplan–Meier method with log-rank tests. In addition, univariate and multivariate Cox regression analyses were performed to identify the significant predictors of OS and DFS for breast cancer patients, and the data were expressed as the hazard ratio (HR) and 95% confidence interval (CI). Data processing and statistical analysis were performed using SPSS implemented with the SPSS version 23.0 (IBM Corp, Armonk, NY, USA). Two-sided t-tests were used for all statistical analyses, and a P-value of <0.05 was regarded as statistically significant.

3 Results

3.1 Expression of SBEM in breast cancer samples and its correlations with clinicopathological characteristics

Immunohistochemical staining showed that SBEM was mainly expressed in the cytomembrane and cytoplasm. Representative images of positive and negative SBEM expression in breast cancer samples are shown in Figure 1. Positive expression of SBEM was detected in 34 of 105 breast cancer tissues (32.4%).

Figure 1 Representative images of SBEM expression in breast cancer tissues: (a) the negative expression of SBEM (×400) and (b) the positive expression of SBEM in immunohistochemical staining (×400).
Figure 1

Representative images of SBEM expression in breast cancer tissues: (a) the negative expression of SBEM (×400) and (b) the positive expression of SBEM in immunohistochemical staining (×400).

The correlations between clinicopathological features and SBEM expression are summarized in Table 1. It is found that the positive expression of SBEM was significantly associated with larger tumor size (P = 0.002), frequent lymph node metastasis (P = 0.029), advanced TNM stage (P = 0.005), negative expression of PR (P = 0.002), and higher Ki-67 index (P = 0.006). However, there were no significant correlations between positive SBEM expression and other clinicopathological parameters such as age, ER status, histological grade, and molecular subtype (P > 0.05).

Table 1

Correlations between clinicopathological variables and the expression of SBEM in breast cancer

Variables Total (N = 105) SBEM expression χ 2 value P value
Positive (N = 34) Negative (N = 71)
Age (years) 0.421 0.517
<50 29 8 (23.5%) 21 (29.6%)
≥50 76 26 (76.5%) 50 (70.4%)
Tumor size (cm) 9.385 0.002
≤2 44 7 (20.0%) 37 (54.7%)
>2 61 27 (80.0%) 34 (45.3%)
Lymph node metastasis 4.792 0.029
No 47 10 (29.4%) 37 (52.1%)
Yes 58 24 (70.6%) 34 (47.9%)
TNM stage — 0.005
I 21 2 (6.7%) 19 (30.2%)
II 71 24 (70.0%) 47 (69.8%)
III 13 8 (23.3%) 5 (0.0%)
Histological grade — 0.885
G1 11 3 (8.8%) 8 (11.3%)
G2 78 25 (73.5%) 53 (74.3%)
G3 16 6 (17.6%) 10 (15.2%)
ER status 3.509 0.061
Negative 42 18 (52.9%) 24 (33.8%)
Positive 63 16 (47.1%) 47 (66.2%)
PR status 9.832 0.002
Negative 54 25 (73.5%) 29 (40.8%)
Positive 51 9 (26.5%) 42 (59.2%)
Molecular subtype — 0.247
HER2 (+) 52 17 (50.0%) 35 (49.3%)
Triple-negative 31 13 (38.2%) 18 (25.4%)
Luminal A 11 1(2.9%) 9(14.1%)
Luminal B 11 3(8.8%) 7(11.3%)
Ki67 (%) 7.510 0.006
<14% 27 3 (6.7%) 24 (26.4%)
≥14% 78 31 (93.9%) 47 (73.6%)
P53 0.626 0.429
Wild 75 26(76.5%) 49 (69.0%)
Mutant 30 8 (23.5%) 22 (31.0%)
Relapse 9.505 0.002
No 76 18 (52.9%) 58 (81.7%)
Yes 29 16 (47.1%) 13 (18.3%)
Death 11.249 0.001
No 91 24 (70.6%) 67 (94.4%)
Yes 14 10 (29.4%) 4 (5.6%)

P < 0.05, 0.01 or 0.001.

3.2 Expression of SBEM and survival outcomes in breast cancer patients

The associations between SBEM expression and survival outcomes in breast cancer patients are shown in Figure 2. The Kaplan–Meier curves indicated that patients with positive expression of SBEM had worse OS and DFS than those with negative expression of SBEM. The 5-year OS rates of patients with positive and negative expression of SBEM were 50.5 and 93.9%, respectively (P < 0.001). The 5-year DFS rates of patients with positive and negative expression of SBEM were 52.8 and 81.7%, respectively (P = 0.001).

Figure 2 Kaplan–Meier curves showed the associations between SBEM expression and survival outcomes of breast cancer patients: (a) for DFS and (b) for OS.
Figure 2

Kaplan–Meier curves showed the associations between SBEM expression and survival outcomes of breast cancer patients: (a) for DFS and (b) for OS.

Univariate and multivariate Cox regression analyses for OS showed that positive expression of SBEM (HR = 1.994, 95% CI: 1.008–3.945, P = 0.047), tumor size (HR = 2.318, 95% CI: 1.071–5.017, P = 0.033), and PR status (HR = 0.195, 95% CI: 0.055–0.694, P = 0.012) were independent prognostic factors for breast cancer patients (Table 2). On the other hand, tumor size (HR = 2.232, 95% CI: 1.084–4.597, P = 0.029) and PR status (HR = 0.235, 95% CI: 0.076–0.730, P = 0.012) were identified as independent predictors of DFS for breast cancer patients. The multivariate analysis did not show an association between positive SBEM expression and DFS, with a HR of 1.773 (95% CI: 0.946–3.322, P = 0.074) (Table 3).

Table 2

Univariate and multivariate Cox analyses for predictors of OS in breast cancer patients

Variables Univariate Cox analysis Multivariate Cox analysis
HR (95% CI) P value HR (95% CI) P value
Age (years) 0.374
<50 Ref
≥50 1.399 (0.668–2.933)
Tumor size (cm) 0.002 0.033
≤2 Ref Ref
>2 2.951 (1.471–5.919) 2.318 (1.071–5.017)
Lymph node metastasis 0.194
No Ref
Yes 1.535 (0.804–2.931)
Histological grade 0.266
G1/G2 Ref
G3 1.637 (0.687–3.902)
ER status 0.436
Negative Ref
Positive 0.783 (0.423–1.448)
PR status 0.039 0.012
Negative Ref Ref
Positive 0.491 (0.250–0.965) 0.195 (0.055–0.694)
Triple-negative breast cancer 0.746
No Ref
Yes 1.109 (0.592–2.081)
Ki67 (%) 0.948
<14% Ref
≥14% 0.974 (0.449–0.449)
P53 0.601
Wild Ref
Mutant 0.827 (0.405–1.687)
SBEM Expression 0.001 0.047
Negative Ref Ref
Positive 2.865 (1.546–5.311) 1.994 (1.008–3.945)

P < 0.05, 0.01 or 0.001.

Table 3

Univariate and multivariate Cox analyses for predictors of DFS in breast cancer patients

Variables Univariate Cox analysis Multivariate Cox analysis
HR (95% CI) P value HR (95% CI) P value
Age (years) 0.756
<50 Ref
≥50 1.110 (0.576–2.138)
Tumor size (cm) 0.002 0.029
≤2 Ref Ref
>2 2.831 (1.463–5.497) 2.232 (1.084–4.597)
Lymph node metastasis 0.524
No Ref
Yes 1.209 (0.675–2.165)
Histological grade 0.102
G1/G2 Ref
G3 1.891 (0.880–4.064)
ER status 0.750
Negative Ref
Positive 0.911 (0.514–1.616)
PR status 0.079 0.012
Negative Ref Ref
Positive 0.570 (0.304–1.068) 0.235 (0.076–0.730)
Triple-negative breast cancer 0.716
No Ref
Yes 0.895 (0.494–1.624)
Ki67 (%) 0.951
<14% Ref
≥14% 1.024 (0.479–2.193)
P53 0.758
Wild Ref
Mutant 0.902 (0.468–1.738)
SBEM expression 0.004 0.074
Negative Ref Ref
Positive 2.314 (1.299–4.124) 1.773 (0.946–3.322)

P < 0.05, 0.01 or 0.001.

4 Discussion

The identification of specific biomarkers is useful for the early diagnosis and clinical management of breast cancer. It has been reported that SBEM is a novel tissue-specific protein in mammary glands, and the expression of SBEM is elevated in human breast cancer [8]. Current evidence indicates that SBEM is usually overexpressed in breast cancer tissues, cell lines, and peripheral blood samples from breast cancer patients [13,14]. Zhang et al. found that both mRNA and protein expression of SBEM was significantly increased in breast cancer tissues [15]. Several studies have reported a significant correlation between high SBEM expression and aggressive clinical features such as tumor size, lymph node metastasis, and clinical stage [11,15]. In a previous study, Liu et al. identified SBEM as a useful biomarker for predicting lymph node metastasis and micrometastasis in breast cancer patients [11]. This suggests that SBEM may provide valuable information for the prognostic assessment of breast cancer. However, little is known about the prognostic value of SBEM in breast cancer patients.

In this study, the protein expression of SBEM in breast cancer tissues was evaluated and the relationships between its expression and clinicopathological characteristics as well as survival outcomes of breast cancer patients were analyzed in an independent patient cohort. The data showed the positive expression of SBEM in 32.4% of samples, and its positive expression was significantly associated with larger tumor size, frequent lymph node metastasis, advanced TNM stage, negative expression of PR, and a higher Ki-67 index. Survival analysis revealed that the positive expression of SBEM was correlated with worse OS and DFS, suggesting its potential as a useful prognostic biomarker for breast cancer patients. These findings are consistent with those of Liu et al., who investigated the expression of SBEM in 87 triple-negative breast cancer (TNBC) tissues using immunohistochemistry, where they observed positive expression of SBEM in 58% of patients and found that it was an independent prognostic marker of OS and DFS in TNBC patients [16]. Moreover, high expression of SBEM was also detected in peripheral blood samples of breast cancer patients compared with healthy controls and other malignancies [11]. In view of its high specificity in breast tissue, evaluation of SBEM levels may thus be helpful for the early diagnosis, risk stratification, and therapeutic management of breast cancer patients.

As a potential oncogene, SBEM plays a critical role in breast cancer metastasis. Functional in vitro experiments demonstrated that the overexpression of SBEM markedly increased both the migration and invasion of MDA-MB-231 and MCF-7 cells [12]. Additionally, the levels of the epithelial marker E-cadherin were reduced, while those of mesenchymal markers such as vimentin, N-cadherin, and Twist were increased by the overexpression of SBEM [12]. These findings suggested that SBEM might promote breast cancer metastasis via the epithelial–mesenchymal transition. However, the biological functions and potential mechanisms of SBEM in the oncogenesis and progression of breast cancer need to be further explored in future research.

5 Conclusions

In conclusion, our findings suggested that the positive expression of SBEM was associated with both aggressive tumor characteristics and poor survival and that SBEM might thus be a valuable prognostic biomarker for breast cancer patients. As a tissue-specific protein, it is necessary to further evaluate the clinical utility of SBEM in larger patient cohorts.

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  1. Funding information: This work was supported by Key Research Plan of Hebei Health Commission (No. 20211395).

  2. Author contributions: H.H. conceived and designed the experiments. H.H. and L.Y. performed the experiments. H.H., B.W., Y.S., and X.L. conducted the statistical analyses. H.H. wrote this manuscript. All authors read and approved the final manuscript submitted for publication.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on a reasonable request.

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Received: 2022-11-27
Revised: 2023-10-28
Accepted: 2023-10-31
Published Online: 2023-11-23

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0784
Keywords for this article
breast cancer; small breast epithelial mucin; immunohistochemistry; prognostic; biomarker
Creative Commons
BY 4.0

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  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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