Identification of Prognosis Biomarkers for High-Grade Serous Ovarian Cancer Based on Stemness

Wang, Zhihang; Yang, Lili; Huang, Zhenyu; Li, Xuan; Qu, Yinwei; Huang, Lan; Wang, Yan

doi:10.3389/fgene.2022.861954

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…Благодаря анализу ключевых генов, связанных со стволовостью в субпопуляциях опухолевых стволовых клеток, были найдены новые перспективные маркеры, тесно связанные с развитием карциномы яичника: LCP2, FCGR3A, COL1A1, COL1A2, MT-CYB, CCT5 и PAPPA [32].…”

Section: Oct4unclassified

Stem cell properties of cancer cells in ascitic fluid of patients with ovarian cancer: a key to control over cancer progression

2023

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Ovarian cancer is considered to be the most malignant and aggressive tumor of the female reproductive system, which is largely associated with early development of malignant ascites and peritoneal carcinomatosis. Cancer cells representing the primary focus, as well as those contained in the ascitic fluid, are extremely heterogeneous in terms of morphological, immunohistochemical, and molecular genetic aspects. Cancer stem cells play a significant role in tumor self-renewal, differentiation, metastasis, and development of chemoresistance.This literature review is aimed at summarizing the available data on cancer stem cells in ovarian cancer and their role in tumor progression. A bioinformatic search was carried out in the PubMed, NCBI, Google Scholar, and eLibrary databases using the keywords “cancer stem cells”, “ovarian cancer”, “malignant ascites”, “chemoresistance”, etc.The data presented in the review make it possible to comprehensively characterize the role of stem cell properties of ovarian cancer cells. The review presents up-to-date information on the molecular and biological parameters of cancer stem cells in ovarian cancer, which are the cellular component of malignant ascites, as well as data from the authors’ studies. Along with this, the article describes modern ideas about the mechanisms of formation of cellular spheroids and their contribution to cancer progression.Cancer stem cells are an extremely promising target in the development of future therapeutic strategies based on the study of signaling pathways in ovarian cancer stem cells, the mechanisms of spheroid formation, and the contribution of immune cells to the acquisition of cancer stem cell properties.

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Section: Oct4unclassified

Stem cell properties of cancer cells in ascitic fluid of patients with ovarian cancer: a key to control over cancer progression

2023

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“…Notably, FTH1 was closely related to the malignant phenotype of ovarian cancer (Jin et al 2023). COL1A2, as a stemness-related key gene, could be used as a prognostic biomarker for HGSOC (Wang et al 2022). In patients with four major tissue types of epithelial ovarian cancer, including HGSOC, COL3A1 expression was associated with shorter overall survival.…”

Section: Pammentioning

confidence: 99%

Prognostic and predictive value of a mRNA signature in high-grade serous ovarian carcinoma with an integrated computation analysis

Yang,

zhou,

Liao

2023

Preprint

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Ovarian cancer (OC) is the leading cause of death among women with gynecologic malignancies. High-grade serous ovarian carcinoma (HGSOC) is the deadliest subtype of OC, accounting for about 90 percent of all ovarian cancer subtypes. Recent studies have shown that HGSOC patients have mutations in proto-oncogenes within the genome. Genome-wide detection and diagnosis are helpful for the diagnosis and treatment of HGSOC. To explore the genomic and transcriptional characteristics of subtypes of HGSOC, achieve accurate typing of tumor types, and obtain genomic characteristics that can reflect the subtypes of HGSOC, Using NMF clustering, SAM, PAM and survival time analysis, copy number variation data and gene expression data of 698 HGSOC samples were analyzed and differential expression genes of different disease subtypes were enriched and analyzed. Functions of genes related to different disease subtypes were enriched. All patients with HGSOC could be stratified into three categories according to genetic variation information and gene expression value. There was significant difference in the survival time curves of patients in different subtypes. And we identified twenty-one genes as the ones with the strongest power to differentiate the samples, including FTH1, COL1A2, COL3A1, GFBP7, ACTB, SPARC, PTTG1IP, TIMP1 and HLA-DPA1. Furthermore, we found that JAK/STAT (Janus kinase and signal transducers and activators of transcription) signaling pathway changes obviously in different subtypes. By investigating the genetic features and gene expression features, subtypes of patients with HGSOC could be accurately judged, which is useful for selecting therapeutic methods.

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“…mRNA expression-based stemness index (mRNAsi), the stemness index of the transcriptome calculated by the OCLR algorithm, could be used to evaluate stemness. Higher mRNAsi scores, as reflected by histopathological grades, are associated with active biological processes in CSCs and with more differentiated tumors 7 .…”

Section: Introductionmentioning

confidence: 99%

scRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma

Lin,

Gao,

et al. 2024

Sci Rep

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Lung adenocarcinoma (LUAD) is one of the sole causes of death in lung cancer patients. This study combined with single-cell RNA-seq analysis to identify tumor stem-related prognostic models to predict the prognosis of lung adenocarcinoma, chemotherapy agents, and immunotherapy efficacy. mRNA expression-based stemness index (mRNAsi) was determined by One Class Linear Regression (OCLR). Differentially expressed genes (DEGs) were detected by limma package. Single-cell RNA-seq analysis in GSE123902 dataset was performed using Seurat package. Weighted Co-Expression Network Analysis (WGCNA) was built by rms package. Cell differentiation ability was determined by CytoTRACE. Cell communication analysis was performed by CellCall and CellChat package. Prognosis model was constructed by 10 machine learning and 101 combinations. Drug predictive analysis was conducted by pRRophetic package. Immune microenvironment landscape was determined by ESTIMATE, MCP-Counter, ssGSEA analysis. Tumor samples have higher mRNAsi, and the high mRNAsi group presents a worse prognosis. Turquoise module was highly correlated with mRNAsi in TCGA-LUAD dataset. scRNA analysis showed that 22 epithelial cell clusters were obtained, and higher CSCs malignant epithelial cells have more complex cellular communication with other cells and presented dedifferentiation phenomenon. Cellular senescence and Hippo signaling pathway are the major difference pathways between high- and low CSCs malignant epithelial cells. The pseudo-temporal analysis shows that cluster1, 2, high CSC epithelial cells, are concentrated at the end of the differentiation trajectory. Finally, 13 genes were obtained by intersecting genes in turquoise module, Top200 genes in hdWGCNA, DEGs in high- and low- mRNAsi group as well as DEGs in tumor samples vs. normal group. Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model. The high-risk group samples had immunosuppressive status, higher tumor malignancy and low benefit from immunotherapy. This work found that malignant tumors and malignant epithelial cells have high CSC characteristics, and identified a model that could predict the prognosis, immune microenvironment, and immunotherapy of LUAD, based on CSC-related genes. These results provided reference value for the clinical diagnosis and treatment of LUAD.

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Identification of Prognosis Biomarkers for High-Grade Serous Ovarian Cancer Based on Stemness

Cited by 7 publications

References 41 publications

Stem cell properties of cancer cells in ascitic fluid of patients with ovarian cancer: a key to control over cancer progression

Stem cell properties of cancer cells in ascitic fluid of patients with ovarian cancer: a key to control over cancer progression

Prognostic and predictive value of a mRNA signature in high-grade serous ovarian carcinoma with an integrated computation analysis

scRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma

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