Objective: More than 75% of ovarian cancer patients are diagnosed at advanced stages and die of tumor cell metastasis. This study aimed to identify new epigenetic and transcriptomic alterations associated with ovarian cancer metastasis. Methods: Two cell sublines with low- and high-metastasis potentials were derived from the ovarian cancer cell line A2780. Genome-wide DNA methylome and transcriptome profiling were carried out in these two sublines by Reduced Representation Bisulfite Sequencing and RNA-seq technologies. Cell-based assays were conducted to support the clinical findings. Results: There are distinct DNA methylation and gene expression patterns between the two cell sublines with low- and high-metastasis potentials. Integrated analysis identified 33 methylation-induced genes potentially involved in ovarian cancer metastasis. The DNA methylation patterns of two of them (i.e., SFRP1 and LIPG) were further validated in human specimens, indicating that they were hypermethylated and downregulated in peritoneal metastatic ovarian carcinoma compared to primary ovarian carcinoma. Patients with lower SFRP1 and LIPG expression tend to have a worse prognosis. Functionally, knockdown of SFRP1 and LIPG promoted cell growth and migration, whereas their overexpression resulted in the opposite effects. In particular, knockdown of SFRP1 could phosphorylate GSK3β and increase β-catenin expression, leading to deregulated activation of the Wnt/β-catenin signaling. Conclusion Many systemic and important epigenetic and transcriptomic alterations occur in the progression of ovarian cancer. In particular, epigenetic silencing of SFRP1 and LIPG is a potential driver event in ovarian cancer metastasis. They can be used as prognostic biomarkers and therapeutic targets for ovarian cancer patients.

ObjectiveTo investigate the influencing factors for chronic pancreatitis （CP） complicated by pancreatogenic portal hypertension （PPH）， and to establish a predictive model. MethodsA retrospective analysis was performed for the clinical data of 99 patients with CP complicated by PPH who were hospitalized in The First Affiliated Hospital of Kunming Medical University， Chuxiong Yi Autonomous Prefecture People’s Hospital， Wenshan People’s Hospital， and Puer People’s Hospital from January 2017 to December 2022， and these patients were enrolled as PPH group. The incidence density sampling method was used to select 198 CP patients from databases as control group. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The Least Absolute Shrinkage and Selection Operator （LASSO） regression model was used to identify the potential predictive factors for CP complicated by PPH， and the predictive factors obtained were included in the multivariate Logistic regression analysis to obtain independent risk factors， which were used to establish a nomogram prediction model. The receiver operating characteristic （ROC） curve， the calibration curve， and the Hosmer-Lemeshow goodness-of-fit test were used to perform internal validation of the model， and the clinical decision curve was used to assess the clinical practicability of the model. ResultsThere were significant differences between the two groups in sex， history of recurrent acute pancreatitis attacks， acute exacerbation of CP， bile duct stones， peripancreatic fluid accumulation， pseudocysts， pulmonary infection， elevated C-reactive protein （CRP）， elevated procalcitonin， fibrinogen （FIB）， neutrophil-lymphocyte ratio （NLR）， gamma-glutamyl transpeptidase， total bilirubin， direct bilirubin， low-density lipoprotein （LDL）， serum amylase， D-dimer， and serum albumin （all P<0.05）. The predictive variables obtained by the LASSO regression analysis included sex， recurrent acute pancreatitis attacks， bile duct stones， peripancreatic fluid accumulation， pulmonary infection， pseudocysts， CRP， NLR， FIB， and LDL. The multivariate Logistic regression analysis showed that sex （odds ratio ［OR］=2.716， P<0.05）， recurrent acute pancreatitis attacks （OR=2.138， P<0.05）， peripancreatic fluid accumulation （OR=2.297， P<0.05）， pseudocysts （OR=2.805， P<0.05）， and FIB （OR=1.313， P<0.05） were independent risk factors for CP complicated by PPH. The above factors were fitted into the model， and the Bootstrap internal validation showed that the nomogram model had an area under the ROC curve of 0.787 （95% confidence interval： 0.730 — 0.844）， and the calibration curve was close to the reference curve. The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good degree of fitting （χ²=7.469， P=0.487）. The clinical decision curve analysis showed that the prediction model had good clinical practicability. ConclusionMale sex， recurrent acute pancreatitis attacks， peripancreatic fluid accumulation， pseudocysts， and FIB are independent risk factors for CP complicated by PPH， and the nomogram model established has good discriminatory ability， calibration， and clinical practicability.

Humans ; Female ; Breast Neoplasms/pathology* ; Multiomics ; Trastuzumab ; Receptor, ErbB-2/genetics* ; Machine Learning