Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.03.30.534465

Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness

Huang, Z.·Byrd, O.·Tan, S.·Knight, B.·Lo, G.·Taylor, L.·Berchuck, A.·Murphy, S. K.
View original source ↗Export metadata ↓

Abstract

Ovarian cancer (OC) is the deadliest reproductive system cancer. Its high lethality is due to the high recurrence rate and the development of chemotherapeutic resistance, which requires synergy between cancer cells and non-cancerous cells of the tumor microenvironment (TME). Analysis of gene expression microarray data from paired primary and recurrent OC tissues revealed significantly elevated expression of the gene encoding periostin (POSTN) in recurrent OC compared to matched primary tumors (p=0.014). Finding POSTN primarily localized to the TME, we investigated the role of TME POSTN in OC cell viability, migration/invasion, and chemosensitivity. Conditioned media with high levels of POSTN (CMPOSTNhigh) was generated using POSTN-transfected fibroblastic preadipocyte 3T3-L1 cells. CMPOSTNhigh-cultured OC cells exhibited faster migration, more invasiveness (p=0.006), and more chemoresistance (p<0.05) compared to OC cells cultured with control medium (CMCTL). Furthermore, CMPOSTNhigh-cultured HEYA8 cells demonstrated increased resistance to paxlitaxel-induced apoptosis. Multiple OC cell lines (HEYA8, CAOV2, and SKOV3) cultured with CMPOSTNhighshowed increases in stem cell side population relative to CMCTL-cultured cells. POSTN-transfected 3T3-L1 cells exhibited more intracellular and extracellular lipids, and this was linked to increased cancer cell expression of the oncogene fatty acid synthetase (FASN). Additionally, POSTN functions in the TME were linked to Akt pathway activities. In a xenograft mouse model of OC, the mean tumor volume in mice injected with CMPOSTNhigh-grown OC cells was larger than that in mice injected with CMCTL-grown OC cells (p=0.0023). Altogether, higher POSTN expression is present in recurrent OC and promotes a more aggressive and chemoresistant oncogenic phenotype in vitro. Within cancer TME fibroblasts, POSTN can stimulate lipid production and is associated with increased OC stem cell side population, consistent with its known role in maintaining stemness. Our results bolster the need for further study of POSTN as a potential therapeutic target in treatment and potential prevention of recurrent ovarian cancer. Author SummaryOvarian cancer has a high rate of recurrent disease that is often resistant to chemotherapy. Comparing primary and recurrent ovarian cancer tumors, we found that the gene POSTN, which encodes the protein periostin, is more highly expressed in recurrent tumors, and more highly expressed in the tumor microenvironment, outside of the cancer cells. We transfected cells with vectors encoding POSTN or with blank vectors to generate conditioned media with high POSTN or control media. Ovarian cancer cells cultured in the POSTN-high conditioned media showed faster wound healing, more invasiveness, and more resistance to apoptosis caused by chemotherapeutic agents, and increased stemness, an important trait in cancer cells, especially recurrent cells. POSTN-transfected cells showed higher expression of the enzyme fatty acid synthase and higher concentrations of lipids, indicating that POSTN may play a role in increasing the energy available to cancer cells. The Akt pathway, often activated in ovarian cancer growth, was activated more in cells cultured in the POSTN-high environment. Finally, we injected immunocompromised mice with ovarian cancer cells that were grown in either the POSTN-high media or the control media, and the average tumor size was higher in mice injected with the cells that were grown in the POSTN-high media.

Source connections

Explore related subjects

cancer biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Huang, Z., Byrd, O., Tan, S., Knight, B., Lo, G., Taylor, L., Berchuck, A., Murphy, S. K.. 2023-04-02. Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness. https://doi.org/10.1101/2023.03.30.534465

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

BAP1 loss and PRAME expression converge to remodel the tumor-immune ecosystem during uveal melanoma progression

Uveal melanoma (UM) is characterized by a small number of recurrent genetic alterations that determine metastatic propensity. BAP1 loss and PRAME expression define the dominant prognostic axes in UM, yet how they promote malignant progression remains unclear. We profiled 190,535 cells from normal uvea, uveal nevus, primary and metastatic UM using single-cell transcriptomics, T cell receptor sequencing, spatial transcriptomics and isogenic perturbation models. Normal melanocytes, nevus cells and UM cells formed a transcriptional continuum marked by loss of differentiation and emergence of neural crest-like, stress-responsive, hypoxic-glycolytic and immune-interacting states. BAP1 loss and PRAME expression imposed distinct but convergent immunoregulatory programs, inducing interferon and TNF-NFkB signaling and MHC-I expression, with HLA-E showing the strongest response. These alterations were accompanied by macrophage and CD8+ T cell remodeling. PRAME-enriched tumor regions formed spatially organized niches enriched for macrophages and plasma cells. These findings define BAP1 loss and PRAME expression as distinct but convergent axes of tumor-immune coevolution and nominate HLA-E as a candidate mediator of immune resistance.

cancer biology↗
bioRxiv2026

A plasma metabolomics workflow for breast cancer detection using quantitative GC/MS and machine learning

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discriminating BC from healthy controls (HC) using conventional machine-learning algorithms. Plasma samples (n = 360; BC = 180, HC = 180) were collected prospectively under standardized preanalytical conditions before surgery and the initiation of systematic anticancer therapy and analyzed using a quantitative GC/MS platform with automated derivatization. Feature selection and model development were conducted using three machine-learning (ML) algorithms (Lasso logistic regression (LR), random forest classifier (RFC), and support vector machine (SVM)). A total of 45 metabolite candidate biomarkers were identified, and the optimal number of metabolite features for each algorithm was estimated by a recursive feature elimination (RFE)-based strategy. The best-performing models achieved area under the ROC curve values (AUC) of 0.910 (LR), 0.893 (RFC), and 0.843 (SVM). We selected prioritizing candidate biomarkers consistently expressed across the multi-algorithm pipeline. A bagging ensemble model improved stability (AUC = 0.911) and reduced false-positive predictions in the independent HC dataset. In addition, model stability with respect to false-positive predictions was assessed using an independent HC cohort (n = 15) that was collected at a separate institution. These results indicate that a plasma metabolomics workflow combined with conventional multi-algorithm ML, algorithm-specific feature selection, and independent assessment provides stable discrimination between BC and HC in a moderately sized cohort.

cancer biology↗
bioRxiv2026

Prognostic value, signal interaction network, and immune infiltration characteristics of MET gene expression in gastric cancer analyzed by multi-database bioinformatics

Objective Based on the bioinformatics method of multi-database integration, this study systematically analyzes the expression characteristics, clinical pathological correlation, prognostic value, potential molecular mechanisms, and immune infiltration patterns of hepatocyte growth factor receptor (MET) in gastric cancer. Methods The UALCAN and GEPIA databases were employed to examine the differential expression of MET between gastric cancer and normal gastric mucosal tissues, as well as its associations with clinicopathological features. Kaplan-Meier Plotter was utilized to evaluate the impact of MET expression on overall survival (OS) and progression-free survival (PFS). Protein-protein interaction (PPI) network was constructed via LinkedOmics, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of co-expressed genes. Four algorithms, TIMER, CIBERSORT, EPIC, and MCPcounter, were used to cross evaluate the correlation between MET expression and immune cell infiltration; Further validate the cell type specific expression of MET using the gastric cancer single-cell sequencing queues (GSE134520, GSE167297) built into the TISCH database. Results MET expression was significantly elevated in gastric cancer tissues compared with normal gastric mucosa (P < .05), and its expression level was significantly correlated with tumor grade and TNM stage. Patients with high MET expression exhibited significantly poorer OS and PFS than those with low MET expression (P < .05). The PPI network revealed that MET could interact with 20 key proteins, including EGFR, ERBB2, HGF, STAT3, and GRB2 etc. GO enrichment analysis suggests that differentially expressed genes are significantly enriched in functions such as the ERBB signaling pathway, cadherin binding, and DNA repair complexes; KEGG enrichment analysis showed that MET related genes were significantly enriched in pathways such as homologous recombination, nuclear cytoplasmic transport, mismatch repair, and oxidative phosphorylation. Immune infiltration analysis showed that the negative association between MET and B cells infiltration has cross algorithm robustness, while the association with neutrophils, CD8+ T cells, CD4+ T cells, and macrophages exhibits algorithmic heterogeneity or insignificance; There is no significant correlation between MET and common immune checkpoint molecules such as PD-1, PD-L1, CTLA4, etc. Single cell validation further confirmed that MET is mainly enriched in malignant epithelial cells and endothelial cells, and is almost not expressed in immune cells. Conclusions Multidimensional bioinformatic analyses demonstrate that elevated MET expression serves as an independent risk factor for unfavorable prognosis in gastric cancer. MET may mediate dual drug resistance in gastric cancer via crosstalk with multiple signaling molecules (including EGFR, ERBB2, HGF, STAT3 and GRB2) and dysregulation of the homologous recombination repair pathway. Results from multiple-algorithm immune infiltration analysis, single-cell dataset analysis and immune checkpoint correlation analysis indicate that MET exerts only modest direct regulatory effects on the gastric cancer immune microenvironment. This exploratory study offers systematic bioinformatic evidence supporting MET as a candidate prognostic biomarker and potential therapeutic target for gastric cancer. Further functional experiments and prospective cohort studies are required to validate its molecular mechanisms and clinical utility.

cancer biology↗