Single-Cell RNA Sequencing Revealed a 3-Gene Panel Predicted the Diagnosis and Prognosis of Thyroid Papillary Carcinoma and Associated With Tumor Immune Microenvironment

Chen, Zuoyu; Wang, Yizeng; Li, Dongyang; Le, Yuting; Han, Yue; Jia, Lanning; Yan, Caigu; Tian, Zhigang; Song, Wan; Li, Fuxin; Zhao, Ke; He, Xianghui

doi:10.3389/fonc.2022.862313

Cited by 8 publications

(13 citation statements)

References 48 publications

(57 reference statements)

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“…Initial insights from scRNA-seq analyses have aided in constructing survival prognostic models through the identification of biomarkers. For instance, three genes-FN1, CLU, and ANXA1-that impact DNA and RNA methylation have been integrated into a prognostic model predicting reduced overall survival for high-risk patients [32]. Additionally, a six-gene prognostic signature-CXCL3, CXCL1, IL1A, CCL5, TNFRSF12A, and IL18 -within the cytokine receptor pathway in PTC has been identified, with increased risk scores associated with decreased overall survival [25].…”

Section: Translational Genomics: Prognostic Models Drug Response and ...mentioning

confidence: 99%

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Tan,

Awuah,

Roy

et al. 2023

Med Oncol

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Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.

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Section: Translational Genomics: Prognostic Models Drug Response and ...mentioning

confidence: 99%

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Tan,

Awuah,

Roy

et al. 2023

Med Oncol

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“…Numerous studies have constructed prognostic models for cancer based on scRNA-seq and bulk RNA-seq datasets in other tumors; however, none have been developed for breast cancer ( 14 - 16 , 43 ). Using the iterative LASSO method, we identified 10 genes that had good prognostic value and could be used to reliably stratify different populations ( 44 ).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we built a prognostic model based on genes that focus on tumor cells screened from scRNA-seq data. This approach has been utilized to study other tumors ( 14 - 16 ); however, breast cancer has not yet been examined.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Ruan¹,

Chi²,

Wang³

et al. 2022

Ann Transl Med

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Background: Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNAseq) is an effective method for studying the changing omics of cells in complex TMEs. Methods:The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.Results: Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.Conclusions: Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor. shinyapps.io/GCNBC/).

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“…Single-cell RNA sequencing (scRNA-seq) has provided a good approach for understanding the TME and immunotherapy [ 13 ]. Integrated analysis of single-cell and bulk RNA sequencing was also provided as a new way to identify prognostic biomarkers and therapeutic targets for cancer [ 14 – 18 ]. Herein, an integrative analysis of scRNA-seq and bulk RNA-seq of HCC was performed to identify T-cell marker genes and develop a prognostic signature, which could be used for prognostic stratification and immunotherapy response in HCC.…”

Section: Introductionmentioning

confidence: 99%

A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing

et al. 2023

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Background Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed malignancy and the third leading cause of cancer death globally. T cells are significantly correlated with the progression, therapy and prognosis of cancer. Limited systematic studies regarding the role of T-cell-related markers in HCC have been performed. Methods T-cell markers were identified with single-cell RNA sequencing (scRNA-seq) data from the GEO database. A prognostic signature was developed with the LASSO algorithm in the TCGA cohort and verified in the GSE14520 cohort. Another three eligible immunotherapy datasets, GSE91061, PRJEB25780 and IMigor210, were used to verify the role of the risk score in the immunotherapy response. Results With 181 T-cell markers identified by scRNA-seq analysis, a 13 T-cell-related gene-based prognostic signature (TRPS) was developed for prognostic prediction, which divided HCC patients into high-risk and low-risk groups according to overall survival, with AUCs of 1 year, 3 years, and 5 years of 0.807, 0.752, and 0.708, respectively. TRPS had the highest C-index compared with the other 10 established prognostic signatures, suggesting a better performance of TRPS in predicting the prognosis of HCC. More importantly, the TRPS risk score was closely correlated with the TIDE score and immunophenoscore. The high-risk score patients had a higher percentage of SD/PD, and CR/PR occurred more frequently in patients with low TRPS-related risk scores in the IMigor210, PRJEB25780 and GSE91061 cohorts. We also constructed a nomogram based on the TRPS, which had high potential for clinical application. Conclusion Our study proposed a novel TRPS for HCC patients, and the TRPS could effectively indicate the prognosis of HCC. It also served as a predictor for immunotherapy.

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Single-Cell RNA Sequencing Revealed a 3-Gene Panel Predicted the Diagnosis and Prognosis of Thyroid Papillary Carcinoma and Associated With Tumor Immune Microenvironment

Cited by 8 publications

References 48 publications

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing

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