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Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models

BACKGROUND: Numerous studies support that Human papillomavirus (HPV) can cause cervical cancer. However, few studies have surveyed the heterogeneity of HPV infected or uninfected (HPV+ and HPV-) cervical cancer (CESC) patients. Integration of scRNA-seq and TCGA data to analyze the heterogeneity of H...

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Autores principales: Wei, Erdong, Reisinger, Amin, Li, Jiahua, French, Lars E., Clanner-Engelshofen, Benjamin, Reinholz, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198569/
https://www.ncbi.nlm.nih.gov/pubmed/35719936
http://dx.doi.org/10.3389/fonc.2022.860900
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author Wei, Erdong
Reisinger, Amin
Li, Jiahua
French, Lars E.
Clanner-Engelshofen, Benjamin
Reinholz, Markus
author_facet Wei, Erdong
Reisinger, Amin
Li, Jiahua
French, Lars E.
Clanner-Engelshofen, Benjamin
Reinholz, Markus
author_sort Wei, Erdong
collection PubMed
description BACKGROUND: Numerous studies support that Human papillomavirus (HPV) can cause cervical cancer. However, few studies have surveyed the heterogeneity of HPV infected or uninfected (HPV+ and HPV-) cervical cancer (CESC) patients. Integration of scRNA-seq and TCGA data to analyze the heterogeneity of HPV+ and HPV- cervical cancer patients on a single-cell level could improve understanding of the cellular mechanisms during HPV-induced cervical cancer. METHODS: CESC scRNA-seq data obtained from the Gene Expression Omnibus (GEO) database and the Seurat, Monocle3 package were used for scRNA-seq data analysis. The ESTIMATE package was used for single-sample gene immune score, CIBERSORT package was used to identify immune scores of cells, and the “WGCNA” package for the weighted correlation network analysis. Univariate Cox and LASSO regression were performed to establish survival and relapse signatures. KEGG and GO analyses were performed for the signature gene. Gene Expression Profiling Interactive Analysis was used for Pan-cancer analysis. RESULTS: In the HPV+ CESC group, CD8+ T cells and B cells were down-regulated, whereas T reg cells, CD4+ T cells, and epithelial cells were up-regulated according to scRNA-seq data. Survival analysis of TCGA-CESC revealed that increased expression of naive B cells or CD8+ T cells favors the survival probability of CESC patients. WGCNA, univariate Cox, and LASSO Cox regression established a 9-genes survival signature and a 7-gene relapse model. Pan-cancer analysis identified IKZF3, FOXP3, and JAK3 had a similar distribution and effects in HPV-associated HNSC. CONCLUSION: Analysis of scRNA-seq and bulk RNA-seq of HPV+ and HPV- CESC samples revealed heterogeneity from transcriptional state to immune infiltration. Survival and relapse models were adjusted according to the heterogeneity of HPV+ and HPV- CESC immune cells to assess the prognostic risk accurately. Hub genes represent similar protection in HPV- associated HNSC while showing irrelevant to other potential HPV-related cancers.
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spelling pubmed-91985692022-06-16 Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models Wei, Erdong Reisinger, Amin Li, Jiahua French, Lars E. Clanner-Engelshofen, Benjamin Reinholz, Markus Front Oncol Oncology BACKGROUND: Numerous studies support that Human papillomavirus (HPV) can cause cervical cancer. However, few studies have surveyed the heterogeneity of HPV infected or uninfected (HPV+ and HPV-) cervical cancer (CESC) patients. Integration of scRNA-seq and TCGA data to analyze the heterogeneity of HPV+ and HPV- cervical cancer patients on a single-cell level could improve understanding of the cellular mechanisms during HPV-induced cervical cancer. METHODS: CESC scRNA-seq data obtained from the Gene Expression Omnibus (GEO) database and the Seurat, Monocle3 package were used for scRNA-seq data analysis. The ESTIMATE package was used for single-sample gene immune score, CIBERSORT package was used to identify immune scores of cells, and the “WGCNA” package for the weighted correlation network analysis. Univariate Cox and LASSO regression were performed to establish survival and relapse signatures. KEGG and GO analyses were performed for the signature gene. Gene Expression Profiling Interactive Analysis was used for Pan-cancer analysis. RESULTS: In the HPV+ CESC group, CD8+ T cells and B cells were down-regulated, whereas T reg cells, CD4+ T cells, and epithelial cells were up-regulated according to scRNA-seq data. Survival analysis of TCGA-CESC revealed that increased expression of naive B cells or CD8+ T cells favors the survival probability of CESC patients. WGCNA, univariate Cox, and LASSO Cox regression established a 9-genes survival signature and a 7-gene relapse model. Pan-cancer analysis identified IKZF3, FOXP3, and JAK3 had a similar distribution and effects in HPV-associated HNSC. CONCLUSION: Analysis of scRNA-seq and bulk RNA-seq of HPV+ and HPV- CESC samples revealed heterogeneity from transcriptional state to immune infiltration. Survival and relapse models were adjusted according to the heterogeneity of HPV+ and HPV- CESC immune cells to assess the prognostic risk accurately. Hub genes represent similar protection in HPV- associated HNSC while showing irrelevant to other potential HPV-related cancers. Frontiers Media S.A. 2022-06-01 /pmc/articles/PMC9198569/ /pubmed/35719936 http://dx.doi.org/10.3389/fonc.2022.860900 Text en Copyright © 2022 Wei, Reisinger, Li, French, Clanner-Engelshofen and Reinholz https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wei, Erdong
Reisinger, Amin
Li, Jiahua
French, Lars E.
Clanner-Engelshofen, Benjamin
Reinholz, Markus
Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models
title Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models
title_full Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models
title_fullStr Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models
title_full_unstemmed Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models
title_short Integration of scRNA-Seq and TCGA RNA-Seq to Analyze the Heterogeneity of HPV+ and HPV- Cervical Cancer Immune Cells and Establish Molecular Risk Models
title_sort integration of scrna-seq and tcga rna-seq to analyze the heterogeneity of hpv+ and hpv- cervical cancer immune cells and establish molecular risk models
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198569/
https://www.ncbi.nlm.nih.gov/pubmed/35719936
http://dx.doi.org/10.3389/fonc.2022.860900
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