Shunjin Zhang , Jiazhuo Yan , Wenjing Pan , Chaoyang Jia , Wei Liu , Sijia Liu , Zhao Wang , Yujie Liu , Yunyan Zhang
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引用次数: 0
Abstract
Ovarian cancer (OC) is women's third most common gynecologic tumor and is highly lethal. Cancer-associated fibroblasts (CAFs) are associated with cancer at all stages of disease progression and are involved in biological processes, including inflammatory processes, tumor development occurrence, and immune rejection. This study aimed to construct prognosis-related CAFs regulatory factors to predict the survival of OC patients. Datasets of OC patients with complete clinical information were collected from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. First, we identified potential regulator factors of CAFs in OC based on the xCell algorithm and weighted gene co-expression analysis (WGCNA). Further screening using one-way cox regression analysis and LASSO regression models yielded 22 prognosis-related CAFs regulatory factors, using which a model was constructed. Subsequently, the diagnostic effectiveness of the model was assessed using receiver operating characteristic (ROC) curves, and the validity of the CAFs regulatory factors survival model was verified in three additional independent datasets and single cell data. Meanwhile, experimental validation was conducted using immunohistochemistry and Western blot. The results showed that GAS1 (Growth arrest specific 1) exhibited a higher expression pattern in fibroblasts from ovarian cancer patients.
The assessment of resistance and immune checkpoint differences across various risk score groups indicates that the CAFs regulatory factor survival model is practical for guiding systemic treatment. In summary, this study establishes a prognostic model composed of 22 CAFs regulatory factors to predict the prognosis of ovarian cancer (OC), providing new perspectives for the clinical treatment of OC.
期刊介绍:
MCP - Advancing biology through–omics and bioinformatic technologies wants to capture outcomes from the current revolution in molecular technologies and sciences. The journal has broadened its scope and embraces any high quality research papers, reviews and opinions in areas including, but not limited to, molecular biology, cell biology, biochemistry, immunology, physiology, epidemiology, ecology, virology, microbiology, parasitology, genetics, evolutionary biology, genomics (including metagenomics), bioinformatics, proteomics, metabolomics, glycomics, and lipidomics. Submissions with a technology-driven focus on understanding normal biological or disease processes as well as conceptual advances and paradigm shifts are particularly encouraged. The Editors welcome fundamental or applied research areas; pre-submission enquiries about advanced draft manuscripts are welcomed. Top quality research and manuscripts will be fast-tracked.