Abstract
This Mendelian randomization (MR) study aims to explore the causal relationships between 91 circulating inflammatory proteins and the incidence and prognosis of breast cancer. Data on exposures were derived from previously published articles, and outcome-related data were obtained from genome-wide association studies databases. The main causal estimates were obtained using the inverse variance weighting (IVW) method. Cochran Q and Rücker Q heterogeneity tests were performed using both IVW and MR-Egger methods. The Egger-intercept method was utilized for pleiotropy testing, single nucleotide polymorphism exclusion tests were conducted for sensitivity analysis, and the F-statistic was calculated to assess the presence of weak instrument bias. Finally, the Mendelian Randomization Pleiotropy RESidual Sum and Outlier method was used to validate the causal associations between circulating inflammatory factors and the incidence and prognosis of breast cancer. A comprehensive analysis of 91 circulating inflammatory proteins revealed their causal associations with the incidence and prognosis of breast cancer. β-Nerve growth factor (NGF), cystatin-C-8, C-X-C motif chemokine (CXCL) 5, IL 15 receptor alpha subunit, IL 22 receptor alpha-1 subunit, matrix metalloproteinase 1 (MMP1), and tumor necrosis factor (TNF) ligand superfamily member 12 showed a causal relationship with the incidence of breast cancer. A positive correlation was observed between β-NGF levels and breast cancer incidence, with an odds ratio (OR) (95% confidence interval (CI)) (IVW method) of 1.1713 (1.0347–1.3259). A negative correlation was found between the levels of cystatin-C-8, CXCL 5, IL 15 receptor alpha subunit, IL 22 receptor alpha-1 subunit, MMP1, and TNF ligand superfamily member 12 and breast cancer incidence, with OR (95% CI) (IVW method) values of 0.8238 (0.7206–0.9416), 0.9117 (0.8471–0.9812), 0.9297 (0.8684–0.9953), 0.8030 (0.6957–0.9269), 0.8580 (0.7719–0.9537), and 0.9175 (0.8437–0.9977), respectively. Furthermore, negative causal associations were found between the levels of C-C motif chemokine 19, IL 18 receptor 1, and TNF-β (also known as lymphotoxin-α) and breast cancer survival, with OR (95% CI) (IVW method) values of 0.8644 (0.7563–0.9880), 0.8565 (0.7786–0.9422), and 0.8889 (0.7940–0.9951), respectively. No pleiotropy was detected in any of the study results. This study elucidates the positive and negative causal relationships between 91 circulating inflammatory proteins and the onset and prognosis of breast cancer.
Keywords: breast cancer, circulating inflammatory proteins, instrumental variables, Mendelian randomization, prognosis
1. Introduction
Breast cancer, an enigmatic malignancy originating from breast tissue, predominantly afflicts women around menopause. By 2020, it had surpassed lung cancer as the most prevalent malignancy among women worldwide.[1] Breast cancer is categorized into luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-positive, and triple-negative types. Advances in medical science and the advent of precision medicine have improved prognoses for breast cancer patients. However, outcomes for HER2-positive and TNBC remain bleak, underscoring the imperative of focusing on this leading female malignancy and investigating prognostic factors.
Aberrant inflammatory responses causing tissue damage are central to many diseases. Inflammatory proteins, metabolites of the inflammatory response, can indicate levels of inflammation and are linked to disease mechanisms.[2] Studies have established a causal relationship between circulating inflammatory proteins and various cardiovascular diseases, malignancies, and autoimmune diseases. The molecular nexus between inflammation proteins and cancer is intricate, influencing oncogenesis, progression, and metastasis in multiple ways. For instance, tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) can promote tumor cell proliferation and survival by activating downstream pathways such as NF-κB and STAT3. Vascular endothelial growth factor facilitates angiogenesis, nourishing tumors for growth and dissemination. Matrix metalloproteinases (MMPs) degrade extracellular matrices, easing cancer cell migration. Moreover, reactive oxygen species (ROS) and other active molecules produced in chronic inflammation can directly damage deoxyribonucleic acid (DNA), leading to mutations and heightened cancer risk. Therefore, exploring the causal link between inflammatory proteins and breast cancer onset and prognosis is crucial for disease detection, understanding molecular pathogenesis, and informing therapeutic strategies.
Mendelian randomization (MR), based on genome-wide association studies (GWAS) databases, employs single nucleotide polymorphisms (SNPs) associated with exposure factors as instrumental variables to investigate causal relationships with disease outcomes. This approach provides evidence akin to that from randomised controlled trials, minimizing confounding factors, and ethical concerns, making it widely applicable for exploring causative associations where randomised controlled trials or large cohort studies are lacking. Thus, MR can theoretically support clinical detection, prognosis prediction, and elucidation of disease mechanisms.[3–6]
2. Materials and methods
2.1. Data sources
The data on the 91 circulating inflammatory proteins considered as exposures in this study were derived from a publicly published article by Zhao J H et al.[7] Outcome-related data were obtained from the GWAS database (https://gwas.mrcieu.ac.uk/), which is publicly accessible, thus obviating the need for ethical committee review. The original studies involving the research data received ethical committee approval. Exposure data were selected according to the criteria (P < 1 × 10−5, r2 ≥ 0.001, distance > 10,000 km). Information related to outcome data is shown in Table 1.
Table 1.
Information on data.
| Exposure/Outcome | Traits | ID | Population | Websites |
|---|---|---|---|---|
| Outcome | Breast cancer | ieu-a-1131 | European | https://gwas.mrcieu.ac.uk/datasets/ieu-a-1131/ |
| Outcome | Breast cancer (survival) | ieu-a-1165 | European | https://gwas.mrcieu.ac.uk/datasets/ieu-a-1165/ |
2.2. Selection of instrumental variables
SNPs employed as instrumental variables must meet the following criteria[8]: The instrumental variable can only affect the outcome through the exposure; The instrumental variable is not influenced by any confounding factors affecting the outcome; and The instrumental variable does not have a direct effect on the outcome. The underlying principles are illustrated in Figure 1.
Figure 1.
Principles of instrumental variable selection.
Initially, the data on 91 circulating inflammatory proteins, obtained from the publicly published article by Zhao J H et al,[7] were screened using a threshold of P < 1 × 10−5. Subsequently, SNPs exhibiting linkage disequilibrium were excluded based on the criteria of r2 ≥ 0.001 and a distance > 10,000 km. Furthermore, the F statistic was calculated using the formula F = R2·(n−k−1)/[k(1−R2)], where R2 represents the correlation between the instrumental variable and the exposure, with an F > 10 serving as the criterion to eliminate weak instrumental variables. In this study, an F-statistic threshold (F > 10) was employed as a filtering criterion to exclude weak instrumental variables, in line with standard recommendations in MR analysis. An F-statistic below 10 indicates weak instruments, which can lead to biased estimates and unreliable conclusions. Setting the threshold at F > 10 helps ensure that the selected instruments are sufficiently strong to provide valid causal estimates, minimizing the risk of weak instrument bias and enhancing the robustness of the results. This threshold is widely accepted in MR studies to ensure the reliability of causal inferences drawn from the data. The selected instrumental variables associated with the 91 exposures were then consolidated, and SNPs related to breast cancer and breast cancer survival outcomes were extracted online from the GWAS database (https://gwas.mrcieu.ac.uk/). Finally, data for the 91 exposures and outcomes were harmonized for a two-sample MR analysis.
2.3. MR analysis
In light of the current state of research, various methods including the inverse variance weighted (IVW) method, MR Egger method, weighted median method, simple mode, and weighted mode are primarily used. This study employs the IVW method as the main approach to investigate the causal relationship between 91 circulating inflammatory proteins and breast cancer as well as breast cancer survival. A P-value < .05 indicates a statistically significant causal association between the exposure and outcome.
2.4. Sensitivity analysis
2.4.1. Heterogeneity test
Heterogeneity is assessed using the inverse variance weighted method and MR-Egger method through Cochran Q and Rücker Q tests. Significant heterogeneity indicated by the Cochran Q test (P < .05) may suggest differences among instrumental variables or that some may be directly associated with the outcome variable (indicating potential omitted variable bias), rather than solely through the exposure. A significant Q statistic necessitates cautious interpretation of results and may require consideration of alternative MR methods to address heterogeneity. The Rücker Q test, an advanced method for assessing global heterogeneity, is particularly suited for evaluating heterogeneity in the presence of multiple instrumental variables. A P-value > .05 suggests no apparent heterogeneity. In case of heterogeneity, the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR PRESSO) method is employed to remove outliers.
2.4.2. Horizontal pleiotropy test
Horizontal pleiotropy is examined using the Egger-intercept method, where a P-value > .05 indicates the absence of horizontal pleiotropy in this study. Moreover, a leave-one-out analysis sequentially excludes one SNP at a time to verify the robustness of the results, observing the impact of the removed SNP on the outcome by comparing each MR analysis’s effect size to the overall effect. Additionally, to mitigate potential confounding effects on heterogeneity and horizontal pleiotropy, each SNP used as an instrumental variable is searched by its rsid number on PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/). SNPs associated with characteristics other than the exposure are excluded, followed by subsequent sensitivity analyses.
2.5. Statistical analysis
Statistical analyses were conducted using R software (version 4.3.1). MR analysis was performed with the “TwoSampleMR” package (version 0.6.22). The creation of figures utilized R packages including “ggplot2,” “plyr,” “dplyr,” “purrr,” “readr,” “pacman,” “ieugwasr,” and “MRInstruments.” Flowcharts were created using the online diagramming website draw.io (https://www.drawio.com/). In the statistical analysis results, a P-value < .05 was considered statistically significant. Clinical trial number: not applicable.
3. Results
3.1. Selection of instrumental variables
During the selection process of instrumental variables, SNPs related to 91 circulating inflammatory proteins were initially extracted from the GWAS database (P < .05). Subsequently, SNPs were excluded based on r2 < 0.001 and distance ≤ 10,000 km to remove those with weak relevance or close proximity that could cause linkage disequilibrium, and an F > 10 criterion was used to eliminate weak instrumental variables. Ultimately, SNPs related to exposures were selected as instrumental variables.
3.2. MR analysis results
The MR analysis, primarily employing the IVW method, demonstrated causal associations between the occurrence of breast cancer and factors such as β-NGF, cystatin C-8, C-X-C motif chemokine (CXCL) 5, IL-15 receptor α subunit, IL-22 receptor α-1 subunit, MMP1, and TNF ligand superfamily member 12. Specifically, a positive correlation exists between β-NGF levels and the incidence of breast cancer, with an odds ratio (OR) (95% confidence interval (CI)) (IVW method) of 1.1713 (1.0347–1.3259). Conversely, cystatin C-8, CXCL 5, IL-15 receptor α subunit, IL-22 receptor α-1 subunit, MMP1, and TNF ligand superfamily member 12 levels are inversely related to breast cancer incidence, with OR (95% CI) (IVW method) respectively being 0.8238 (0.7206–0.9416), 0.9117 (0.8471–0.9812), 0.9297 (0.8684–0.9953), 0.8030 (0.6957–0.9269), 0.8580 (0.7719–0.9537), and 0.9175 (0.8437–0.9977). The results are depicted in Figure 2. Additionally, levels of C-C motif chemokine (CCL) 19, IL-18 receptor 1, and TNF-β (also known as lymphotoxin-α) exhibit a negative causal relationship with breast cancer survival, with OR (95% CI) (IVW method) respectively being 0.8644 (0.7563–0.9880), 0.8565 (0.7786–0.9422), and 0.8889 (0.7940–0.9951). The results are shown in Figure 3.
Figure 2.
This forest plot is based on Mendelian randomization (MR) analysis results, with 91 circulating inflammatory proteins as exposures and breast cancer as the outcome. The figure presents results from 5 MR analysis methods: inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode, with IVW as the primary method of analysis. The vertical red line on the right represents the reference line where the odds ratio (OR) value is 1. Segments located to the left of this reference line indicate a negative correlation between exposure and outcome, whereas segments located to the right suggest a positive correlation between exposure and outcome.
Figure 3.
This forest plot displays the Mendelian randomization (MR) analysis results, with 91 circulating inflammatory proteins as exposures and breast cancer survival as the outcome. The figure illustrates results from 5 different MR analysis methods: inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode, with IVW serving as the primary analytical approach. The vertical red line on the right side denotes the reference line where the odds ratio (OR) value is 1. Segments located to the left of this reference line indicate a negative correlation between exposure and outcome, suggesting that the exposure is associated with improved survival. Conversely, segments to the right of the line suggest a positive correlation, indicating that the exposure is associated with decreased survival.
3.3. Sensitivity analysis
Sensitivity analysis was conducted using the inverse variance weighted (IVW) and MR-Egger methods, with Cochran Q and Rücker Q tests employed to assess heterogeneity. P > .05 was considered indicative of the absence of heterogeneity. When heterogeneity was detected, the MR PRESSO method was applied, using 1000 permutations to exclude outliers. Horizontal pleiotropy was evaluated using the Egger intercept method, with P > .05 suggesting no evidence of horizontal pleiotropy in this study. The MR analysis results remained statistically significant after validation with the MR PRESSO method.
4. Discussion
This two-sample MR study is the first to explore the causal relationships between 91 circulating inflammatory proteins and breast cancer incidence and prognosis.[9,10] The study conducted heterogeneity tests, horizontal pleiotropy tests, and sensitivity analyses, including leave-one-out analysis, to verify the robustness of the results. Forest plots and scatter plots were used to illustrate the findings, with MR PRESSO applied for further validation. All MR results were confirmed through MR PRESSO, which reinforced the robustness of the conclusions. The results revealed that the level of β-NGF was positively associated with breast cancer incidence, while caspase-8, CXCL 5, IL-15 receptor α subunit, IL-22 receptor α-1 subunit, MMP1, and TNF ligand superfamily member 12 were negatively correlated with breast cancer incidence. Furthermore, CCL 19, IL-18 receptor 1, and TNF-β (also known as lymphotoxin-α) levels were found to have a negative causal association with breast cancer survival. These findings provide theoretical support for the detection and screening of breast cancer incidence. Additionally, circulating inflammatory proteins associated with breast cancer survival may offer insights into the prognostic features of surgical treatment or chemotherapy in breast cancer patients. The 7 circulating inflammatory proteins associated with breast cancer incidence also open new directions for understanding the molecular mechanisms underlying breast cancer development.
To date, some observational studies have addressed the relationship between breast cancer and inflammatory markers, but the specific associations and causal mechanisms remain poorly understood. Moreover, there is a lack of randomized controlled trials and molecular-based experiments to reveal the causal connections between circulating inflammatory proteins and the molecular mechanisms underlying breast cancer incidence. A review study by Jing Peng et al[11] highlights common inflammatory biomarkers involved in breast cancer diagnosis and treatment, including HER2, PD-L1, CD4, CD6, IL-1β, IL-6, IL-8, IGF-1, and vascular endothelial growth factor. Additionally, IL-6, a representative inflammatory marker of the IL superfamily, plays a significant role in breast cancer diagnosis, treatment, and intervention, exhibiting considerable variability. Studies by Hiensch et al and Dieli-Conwright et al[12] suggest that exercise can reduce IL-6 levels in breast cancer patients and survivors. Conversely, other studies[13,14] have shown that IL-6 levels significantly increase following chemotherapy. TNF is the second most significant inflammatory marker associated with breast cancer. Several studies[15–20] indicate that lower levels of TNF markers are associated with better disease prognosis or treatment outcomes. Moreover, research has found that the TNF-β + 252GG genotype is more common in Turkish breast cancer patients than in controls, which aligns with our study’s finding of a negative causal association between TNF-β (lymphotoxin-α) levels and breast cancer survival. A proteomic analysis by Alakesh Bera et al revealed that elevated levels of MMP family members (such as MMP2 and MMP9) are associated with breast cancer progression, which seems to contradict our study’s finding of a negative causal relationship between MMP1 levels and breast cancer incidence. The underlying molecular mechanisms require further exploration.
Further literature research on the inflammatory proteins related to this study reveals that nerve growth factor (NGF)[21] is a group of proteins that play a crucial role in the growth, survival, and differentiation of central and peripheral neurons, as well as in programmed cell death. The causal link between NGF and breast cancer is mediated through the NGF signaling pathway, where NGF is synthesized and released by breast cancer cells. It activates various signaling cascades involving TrkA and NGFR/p75NTR receptors, promoting cell division, anti-apoptosis, and angiogenesis. Studies suggest that NGF and its receptors may be a key potential therapeutic target in breast cancer treatment, further supporting the positive causal relationship between NGF-α levels and breast cancer incidence observed in our study. The relationship between inflammatory markers and breast cancer incidence, prognosis, and recurrence is complex, but existing research consistently indicates that C-reactive protein levels, IL-6, TNF-β, and IL-1β are strongly associated with increased breast cancer incidence, invasiveness, and poor prognosis. Persistent low-grade inflammation may enhance nutrient signaling, increase mutation rates, and promote the growth of mutated cells. In addition to directly stimulating cell growth, activated inflammatory cells also promote the accumulation of reactive oxygen species (ROS) and reactive nitrogen species intermediates in surrounding cells, which can directly or indirectly damage DNA and its encoded proteins, thereby facilitating tumor development.[22] However, the specific tumor-inducing molecular mechanisms of different circulating inflammatory proteins through distinct signaling pathways still require further investigation by researchers.
This study provides a comprehensive analysis of the causal relationships between 91 circulating inflammatory proteins and breast cancer incidence and prognosis. Using MR, we identified several inflammatory proteins, including β-NGF, caspase-8, CXCL 5, IL-15 receptor α subunit, IL-22 receptor α-1 subunit, MMP1, and TNF ligand superfamily member 12, all of which were causally associated with breast cancer development. Additionally, negative causal associations between CCL 19, IL-18 receptor 1, and TNF-β (also known as lymphotoxin-α) with breast cancer survival were confirmed, further underscoring the complex role of inflammation in both cancer initiation and progression.
These findings suggest that inflammatory markers may play a pivotal role in the pathogenesis and prognosis of breast cancer, providing new insights for risk stratification and therapeutic interventions. Notably, several negatively associated proteins (such as caspase-8 and MMP1) emphasize their protective roles in breast cancer development, while proteins like β-NGF contribute significantly to cancer initiation.
Although this study focused on the overall association between circulating inflammatory proteins and breast cancer, it is essential to consider potential differences across molecular subtypes of breast cancer due to its inherent heterogeneity. Breast cancer is typically classified into subtypes based on estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and triple-negative (TNBC) status, each with distinct pathogenic mechanisms, prognoses, and responses to treatment. Consequently, the expression patterns of inflammatory proteins may vary significantly across these subtypes, leading to differential causal effects on breast cancer development and prognosis.
Estrogen receptor-positive (HR+) breast cancer generally exhibits a longer survival rate, but in some cases, inflammatory responses may accelerate tumor progression and metastasis, particularly when the immune response is dysregulated. HER2-positive breast cancer, on the other hand, is more aggressive, with inflammatory factors potentially playing a role in tumor growth or resistance to targeted therapies. Meanwhile, in triple-negative breast cancer (TNBC), where effective targeted treatments are still limited, inflammatory pathways may be particularly important in driving the aggressiveness of the disease.
Given that several inflammatory proteins significantly influence both the incidence and prognosis of breast cancer, further longitudinal studies are warranted to assess their potential as predictive biomarkers. Such studies would not only facilitate early diagnosis but also improve risk stratification and prognostic predictions for breast cancer patients, enabling more effective treatment strategies.
In conclusion, this study highlights the importance of inflammatory proteins as potential biomarkers for breast cancer risk and prognosis, as well as targets for future therapeutic interventions. Understanding the molecular heterogeneity of breast cancer and its relationship with inflammation will ultimately help improve personalized treatment strategies and provide better outcomes for breast cancer patients.
This study has the following strengths: It comprehensively examined 91 circulating inflammatory proteins as exposures to explore their causal relationships with breast cancer incidence and prognosis; It employed heterogeneity and horizontal pleiotropy tests to confirm the scientific validity of the study, and used MR PRESSO to validate the conclusions; and The study presented the results of 5 MR analyses using forest plots, confirming the reliability of the findings.
However, the study also has some limitations: The study employed a unidirectional two-sample MR design without reverse analysis, which limits the ability to reveal the circulating inflammatory protein levels in breast cancer patients; The study population is limited to Europeans, which restricts the generalizability of the conclusions; and The MR analysis was conducted on all breast cancer patients as a group without distinguishing different molecular subtypes. Due to the limitations of the GWAS database, further analysis can only be performed on subgroups with different expression statuses of ER and HER2; and The study did not combine other public databases or observational cohort studies to compare circulating inflammatory protein levels between breast cancer patients and healthy individuals, thus lacking further validation and leaving the theoretical evidence somewhat thin.
5. Conclusion
Circulating inflammatory proteins have a definite causal association with the incidence and prognosis of breast cancer. Prophylactic testing of these proteins in populations at risk or diagnosed with breast cancer can predict disease occurrence and patient outcomes, holding significant clinical importance.
Author contributions
Data curation: Yulai Yin.
Formal analysis: Zhongyue Li.
Methodology: Zhongyue Li, Yulai Yin, Quanbin Men.
Software: Zhongyue Li, Yulai Yin, Quanbin Men.
Validation: Zhongyue Li, Yulai Yin, Quanbin Men.
Visualization: Zhongyue Li, Yulai Yin.
Writing – original draft: Zhongyue Li, Yulai Yin.
Writing – review & editing: Quanbin Men.
Abbreviations:
- CCL
- C-C motif chemokine
- CI
- confidence interval
- Cochran Q =
- Cochran Q heterogeneity test
- CXCL
- C-X-C motif chemokine
- GWAS
- genome-wide association studies
- IL
- interleukin
- IVW
- inverse variance weighting
- MMP
- matrix metalloproteinase
- MR PRESSO
- Mendelian Randomization Pleiotropy RESidual Sum and Outlier
- MR
- Mendelian randomization
- MR-Egger
- MR-Egger regression method
- OR
- odds ratio
- Rücker Q =
- Rücker Q heterogeneity test
- SNP
- single nucleotide polymorphism
- TNF
- tumor necrosis factor
The data on the 91 circulating inflammatory proteins considered as exposures in this study were derived from a publicly published article by ZHAO J H et al.[7] Outcome-related data were obtained from the GWAS database (https://gwas.mrcieu.ac.uk/), which is publicly accessible, thus obviating the need for ethical committee review. The ethical statement for the original study associated with the dataset ieu-a-1131 is as follows: A subset of the data that supports the findings of this study is publicly available via dbGaP (www.ncbi.nlm.nih.gov/gap; accession number phs001265.v1.p1). The complete dataset will not be made publicly available due to restrictions imposed by the ethics committees of individual studies; requests for data can be made to the corresponding author or the Data Access Coordination Committee (DACCs) of BCAC (http://bcac.ccge.medschl.cam.ac.uk/): BCAC DACC approval is required to access data from studies ABCFS, ABCS, ABCTB, BBCC, BBCS, BCEES, BCFR-NY, BCFR-PA, BCFR-UT, BCINIS, BSUCH, CBCS, CECILE, CGPS, CTS, DIETCOMPLYF, ESTHER, GC-HBOC, GENICA, GEPARSIXTO, GESBC, HABCS, HCSC, HEBCS, HMBCS, HUBCS, KARBAC, KBCP, LMBC, MABCS, MARIE, MBCSG, MCBCS, MISS, MMHS, MTLGEBCS, NC-BCFR, OFBCR, ORIGO, pKARMA, POSH, PREFACE, RBCS, SKKDKFZS, SUCCESSB, SUCCESSC, SZBCS, TNBCC, UCIBCS, UKBGS, and UKOPS.Summary results for all variants are available at http://bcac.ccge.medschl.cam.ac.uk/. Requests for further data should be made through the BCAC Data Access Co-ordinating Committee (http://bcac.ccge.medschl.cam.ac.uk/). The ethical statement for the original study associated with the dataset ieu-a-1165 is as follows: All studies were approved by the relevant institutional review boards, and all participants provided written informed consent.
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Li Z, Yin Y, Men Q. Investigating the causal linkages between circulating inflammatory proteins and breast cancer risk and prognosis: Insights from a two-sample Mendelian randomization analysis. Medicine 2026;105:17(e48402).
Contributor Information
Zhongyue Li, Email: 695924998@qq.com.
Yulai Yin, Email: 1972688583@qq.com.
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