Shuran Chen

Background: The causal relationship between modifiable risk factors and inflammatory disorders of the breast (IDB) remains unclear, posing a challenge to the early identification and intervention of patients with IDB. The present study aimed to clarify the causal association between 30 predominant risk factors and IDB. Methods: Single-variable MR (SVMR) and multi-variable MR (MVMR) analyses were used to investigate the causal association between 30 modifiable risk factors and IDB. Inverse-variance weighting (IVW), MR‒Egger, and weighted median (WM) were further supported by several sensitivity analyses. Results: Genetically predicted Adult BMI females, body fat percentage, trunk fat mass, Waist circumference, Overall health rating, Medication use (glucocorticoids), and smoking status were found to increase the risk of IDB (all P < 0.05). No significant association was detected between interleukin levels, autoimmune diseases, pregnancy, infection and IDB. Multivariate MR identified body fat percentage, and smoking status as risk factors for IDB. Conclusions: We are the first to use MR analysis to explore the causal relationships between modifiable risk factors and IDB, revealing smoking status and body fat percentage at the genetic level are causally associated with a higher risk of IDB.

Background: Liver metastasis of CRC is still the main cause of poor prognosis in patients with CRC. Previous studies have suggested that serpin family C member 1(SERPINC1) is involved in the development of a variety of tumours, but its effect on colorectal cancer progression has been poorly elucidated.Methods: Based on the GEO database, this study identifies the core gene SERPINC1 associated with liver metastasis in CRC. We used transcriptomic data and immunohistochemical staining to explore the expression of SERPINC1 in normal, cancer, and liver metastases tissue from CRC patients. Clinical data obtained from our hospital were used to explore the impact of SERPINC1 on the prognosis of colon cancer patients. Mechanistically, the biological functions exerted by SERPINC1 in CRC were predicted by bioinformatics, and the results were validated by the results of the experiments in vitro. Cell lines with knockdown of SERPINC1 were performed a series assay such as trans well, CCK‐8 and colony formation assay to explore the relationship between SERPINC1 and proliferation and metastasis of CRC cells. Finally, the effect of SERPINC1 on the sensitivity of colon cancer patients to immune checkpoint therapy was evaluated.Results: In CRC liver metastatic tissues, we found significantly high expression of SERPINC1. Briefly, 212 CRC cohorts showed that SERPINC1 was significantly associated with TNM stage and plasma CA19‐9 and CEA in CRC patients. Univariate and multivariate Cox demonstrated that SERPINC1 was significantly associated with 5‐year survival after radical surgery for colorectal cancer (p < 0.001). Bioinformatics predicted that SERPINC1 affects metastasis of colon cancer through epithelial‐mesenchymal transition (EMT). Colony formation assay and CCK‐8 assay showed that SERPINC1 promotes malignant proliferation of CRC cells, trans well assay showed that SERPINC1 promotes distant migratory behaviour of CRC cells and protein blotting assay showed that SERPINC1 may promote migration by promoting the TGF‐β1‐mediated EMT of CRC cells. In addition, several immunotherapy cohorts also reflected that the expression of SERPINC1 reduced the sensitivity of CRC patients to immune checkpoint therapy.Conclusion: Our study identified SERPINC1 as a novel liver metastasis‐associated gene in CRC. Targeting SERPINC1 may be a novel therapeutic strategy for patients with liver metastases from CRC.

Background: Gastric cancer(GC) is a common and poor prognosis malignant solid tumour, the application of immunotherapy has improved the prognosis of patients greatly, but the precise immunotherapy strategy is still the difficult problem of immunotherapy. Mendel randomization is a way to demonstrate exposure and outcomes at a causal level. Mendel randomization trial is therefore an excellent way of looking at the causal link between immune cells and the risk of GC, with the aim of providing new insights into immunotherapy regimens for GC patients. Method: Genetic instrumental variables(IVs) for GC were obtained from a genome wide association study (GWAS). Inverse-variance weighted (IVW) method was used as the primary analysis. Simple-median method, weighted-median method, MR-Egger regression, and MR pleiotropy residual sum and outlier test were used as the further analysis. Uni-cox and lasso were used to select genes related to GC and survival model was constructed. Through many data of GC patients, we further explored the relationship between risk models and prognosis of GC patients, association between tumour mutation load(TMB) and microsatellite instability state(MSI). Result: We obtained 731 immune cells from previous studies. From these cells, we screened six immune cells closely associated with GC. Based on the SNPs of 6 immune cells, we obtained 77 genes. After Cox and lasso analysis, we obtained 5 immune cell related genes. The 5-gene risk model can divide patients into two GC groups with different prognosis. High risk GC patients had higher suppressive immune cell infiltration, lower TMB, lower tumour cell stemness, and higher TIDE scores. Conclusion: This study based on a variety of data from MR analysis demonstrated the association between immune cells and GC risk at the causal level. 6 immune cell-related genes are associated with TMB, MSI and immunotherapy sensitivity in GC patients.

Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR‐related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A‐modified DDR‐related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan‐Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C‐index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR‐related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low‐risk group were significantly better than those in the high‐risk group. Conclusion. We screened eight DDR‐related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.