Characterization of immature ovarian teratomas through single-cell transcriptome

Cao, Minyuan; Deng, Yun; Deng, Yu; Wu, Jing; Yang, Chongyi; Wang, Zijun; Hou, Qing; Fu, Huancheng; Ren, Zhixiang; Xia, Xuyang; Li, Yue; Wang, Wei; Xu, Heng; Liao, Xin; Shu, Yang

doi:10.3389/fimmu.2023.1131814

Cited by 2 publications

(2 citation statements)

References 60 publications

(57 reference statements)

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“…The results from our pathway and functional enrichment analyses underscore the complex interplay between Gene SOX2 expression and the intricate biological processes within cervical squamous cell carcinoma. The enrichment of immune-related pathways and processes suggests a potential role of Gene SOX2 in modulating the immune microenvironment and tumor progression [43].…”

Section: Discussionmentioning

confidence: 99%

The Role of SOX 2 Gene in Cervical Cancer: Focus on Iron Death and Construct a Predictive Model

Liu,

Wei,

Ding

2024

Preprint

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Background The intricate interplay between stemness markers and cell death pathways significantly influences the pathophysiology of cervical cancer. SOX2, a pivotal regulator of stem cell pluripotency, has recently been implicated in the modulation of ferroptosis, a specialized form of iron-dependent cell death, in cancer dynamics. This study delineates the role of SOX2 in the ferroptotic landscape of cervical carcinoma. Objective To delineate the association between SOX2 expression and ferroptosis in cervical cancer and develop a robust, SOX2-centric predictive model for patient prognosis, enhancing personalized therapeutic approaches. Methods A multi-dimensional approach integrating advanced bioinformatics, comprehensive molecular profiling, and state-of-the-art machine learning algorithms was employed to assess SOX2 expression patterns and their correlation with ferroptotic markers in cervical cancer tissues. The prognostic model was meticulously crafted, incorporating SOX2 expression levels and ferroptosis indicators. Results The investigation unveiled a profound and intricate correlation between SOX2 expression and ferroptotic processes in cervical cancer, substantiated by robust molecular evidence. The developed predictive model, underpinned by SOX2 expression, exhibited superior predictive accuracy for patient prognosis, potentially guiding therapeutic decision-making. Conclusion This study underscores the critical role of SOX2 in orchestrating the ferroptotic pathway in cervical cancer, presenting a novel prognostic framework. The SOX2-centric predictive model emerges as a significant advancement in prognostic evaluation, offering a gateway to precision oncology in the realm of gynecologic oncology.

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Section: Discussionmentioning

confidence: 99%

The Role of SOX 2 Gene in Cervical Cancer: Focus on Iron Death and Construct a Predictive Model

Liu,

Wei,

Ding

2024

Preprint

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“…For the bulk RNAseq data, genes with counts less than 10 for more than 70% of the samples were removed from analyses. For the scRNAseq data, cells with UMI numbers below 500, gene numbers below 300 or greater than 6,000, or mitochondrial-derived UMI counts of more than 15% were considered low-quality and were removed [103].…”

mentioning

confidence: 99%

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

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BackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only ∼17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible.MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets.ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such asEPCAM, UBE2C, CHD1L, TP53,CD24,WFDC2, andFANCI,were confirmed. We also identified novel predictive coregulating weak genes includingGIGYF2, GNPAT, RAD54L, andELL.Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGGtight junction, ribosome, andcell cyclepathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types.ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.

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Characterization of immature ovarian teratomas through single-cell transcriptome

Cited by 2 publications

References 60 publications

The Role of SOX 2 Gene in Cervical Cancer: Focus on Iron Death and Construct a Predictive Model

The Role of SOX 2 Gene in Cervical Cancer: Focus on Iron Death and Construct a Predictive Model

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

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