Dataset Information

Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma.

ABSTRACT: Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.

SUBMITTER: Su M

PROVIDER: S-EPMC7890365 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma.

Su Miao M Qiao Kuang-Yuan KY Xie Xiao-Li XL Zhu Xin-Ying XY Gao Fu-Lai FL Li Chang-Juan CJ Zhao Dong-Qiang DQ

Frontiers in genetics 20210204

Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were process ...[more]

PMID: 33613623

Similar Datasets

Project description:Importance:In patients with intrahepatic cholangiocarcinoma (ICC), the oncologic benefit of surgery and perioperative outcomes for large multifocal tumors or tumors with contiguous organ involvement remain to be defined. Objectives:To develop and externally validate a simplified prognostic score for ICC and to determine perioperative outcomes for large multifocal ICCs or tumors with contiguous organ involvement. Design, Setting, and Participants:This study of a contemporary cohort merged data from the California Cancer Registry (January 1, 2004, through December 31, 2011) and the Office of Statewide Health Planning and Development inpatient database. Clinicopathologic variables were compared between tumors that were intrahepatic, small (<7 cm), and solitary (ISS) and those that had extrahepatic extension and were large (?7 cm) and multifocal (ELM). External validation of the prognostic model was performed using an independent data set from the National Cancer Institute's Surveillance, Epidemiology, and End Results database from January 1, 2004, through December 31, 2013. Main Outcomes and Measures:Patient overall survival after hepatectomy. Results:A total of 275 patients (123 men [44.7%] and 152 women [55.3%]; median [interquartile range] age, 65 [55-72] years) met the inclusion criteria. No significant differences in overall complication rate (ISS, 48 [34.5%]; ELM, 37 [27.2%]; P?=?.19) and mortality rate (ISS, 10 [7.2%]; ELM, 6 [4.4%]; P?=?.32) were found. A multivariate Cox proportional hazards model demonstrated that multifocality, extrahepatic extension, grade, node positivity, and age greater than 60 years are independently associated with worse overall survival. These variables were used to develop the MEGNA prognostic score. The prognostic separation/discrimination index was improved with the MEGNA prognostic score (0.21; 95% CI, 0.11-0.33) compared with the staging systems of the American Joint Committee on Cancer sixth (0.17; 95% CI, 0.09-0.29) and seventh (0.18; 95% CI, 0.08-0.30) editions. Conclusions and Relevance:The MEGNA prognostic score allows more accurate and superior estimation of patient survival after hepatectomy compared with current staging systems.

Project description:BackgroundLong non-coding RNA (lncRNA) plays a critical role in the malignant progression of intrahepatic cholangiocarcinoma (iCCA). This study aimed to establish a 4-lncRNA prognostic signature and explore corresponding potential mechanisms in patients with iCCA.MethodsThe original lncRNA-seq and clinical data were collected from the TCGA and GEO databases. Overlapping and differentially expressed lncRNAs (DE-lncRNAs) were further identified from transcriptome data. Univariate regression analysis was performed to screen survival-related DE-lncRNAs, which were further selected to develop an optimal signature to predict prognosis using multivariate regression analysis. The Kaplan-Meier survival curve visualized the discrimination of the signature on overall survival (OS). The area under the curve (AUC) and C-index were used to verify the predictive accuracy of the signature. Combined with clinical data, multivariate survival analysis was used to reveal the independent predictive capability of the signature. In addition, a prognostic nomogram was constructed. Finally, the common target genes of 4 lncRNAs were predicted by the co-expression method, and the corresponding functions were annotated by GO and KEGG enrichment analysis. Gene set enrichment analysis (GSEA) was also performed to explore the potential mechanism of the signature. Quantitative real-time PCR was used to evaluated the expression of 4 lncRNAs in an independent cohort.ResultsWe identified and constructed a 4-lncRNA (AC138430.1, AGAP2-AS1, AP001783.1, and AP005233.2) prognostic signature using regression analysis, and it had the capability to independently predict prognosis. The AUCs were 0.952, 0.909, and 0.882 at 1, 2, and 3 years, respectively, and the C-index was 0.808, which showed good predictive capability. Subsequently, combined with clinical data, we constructed a nomogram with good clinical application. Finally, 252 target genes of all four lncRNAs were identified by the co-expression method, and functional enrichment analysis showed that the signature was strongly correlated with metabolism-related mechanisms in tumourigenesis. The same results were also validated via GSEA.ConclusionWe demonstrated that a metabolism-related 4-lncRNA prognostic signature could be a novel biomarker and deeply explored the target genes and potential mechanism. This study will provide a promising therapeutic strategy for patients with intrahepatic cholangiocarcinoma.

Project description:Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer. It is defined by cholangiocytic differentiation and has poor prognosis. Recently, epigenetic processes have been shown to play an important role in cholangiocarcinogenesis. We performed an integrative analysis on 52 iCCAs using both genetic and epigenetic data with a specific focus on DNA methylation components. We found recurrent isocitrate dehydrogenase 1 (IDH1) and IDH2 (28%) gene mutations, recurrent arm-length copy number alterations (CNAs), and focal alterations such as deletion of 3p21 or amplification of 12q15, which affect BRCA1 Associated Protein 1, polybromo 1, and mouse double minute 2 homolog. DNA methylome analysis revealed excessive hypermethylation of iCCA, affecting primarily the bivalent genomic regions marked with both active and repressive histone modifications. Integrative clustering of genetic and epigenetic data identified four iCCA subgroups with prognostic relevance further designated as IDH, high (H), medium (M), and low (L) alteration groups. The IDH group consisted of all samples with IDH1 or IDH2 mutations and showed, together with the H group, a highly disrupted genome, characterized by frequent deletions of chromosome arms 3p and 6q. Both groups showed excessive hypermethylation with distinct patterns. The M group showed intermediate characteristics regarding both genetic and epigenetic marks, whereas the L group exhibited few methylation changes and mutations and a lack of CNAs. Methylation-based latent component analysis of cell-type composition identified differences among these four groups. Prognosis of the H and M groups was significantly worse than that of the L group. Conclusion: Using an integrative genomic and epigenomic analysis approach, we identified four major iCCA subgroups with widespread genomic and epigenomic differences and prognostic implications. Furthermore, our data suggest differences in the cell-of-origin of the iCCA subtypes.

Project description:BackgroundThe tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets.MethodsThe scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets.ResultsWe clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate.ConclusionOur observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.

Project description:Background and objectivesCurrently, the prognostic performance of the staging systems proposed by the 8th edition of the American Joint Committee on Cancer (AJCC 8th) and the Liver Cancer Study Group of Japan (LCSGJ) in resectable intrahepatic cholangiocarcinoma (ICC) remains controversial. The aim of this study was to use machine learning techniques to modify existing ICC staging strategies based on clinical data and to demonstrate the accuracy and discrimination capacity in prognostic prediction.Patients and methodsThis is a retrospective study based on 1,390 patients who underwent surgical resection for ICC at Eastern Hepatobiliary Surgery Hospital from 2007 to 2015. External validation was performed for patients from 2015 to 2017. The ensemble of three machine learning algorithms was used to select the most important prognostic factors and stepwise Cox regression was employed to derive a modified scoring system. The discriminative ability and predictive accuracy were assessed using the Concordance Index (C-index) and Brier Score (BS). The results were externally validated through a cohort of 42 patients operated on from the same institution.ResultsSix independent prognosis factors were selected and incorporated in the modified scoring system, including carcinoembryonic antigen, carbohydrate antigen 19-9, alpha-fetoprotein, prealbumin, T and N of ICC staging category in 8th edition of AJCC. The proposed scoring system showed a more favorable discriminatory ability and model performance than the AJCC 8th and LCSGJ staging systems, with a higher C-index of 0.693 (95% CI, 0.663-0.723) in the internal validation cohort and 0.671 (95% CI, 0.602-0.740) in the external validation cohort, which was then confirmed with lower BS (0.103 in internal validation cohort and 0.169 in external validation cohort). Meanwhile, machine learning techniques for variable selection together with stepwise Cox regression for survival analysis shows a better prognostic accuracy than using stepwise Cox regression method only.ConclusionsThis study put forward a modified ICC scoring system based on prognosis factors selection incorporated with machine learning, for individualized prognosis evaluation in patients with ICC.

Dataset Information

Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma.

Publications

Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets