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Pan-sarcoma characterization of lncRNAs in the crosstalk of EMT and tumour immunity identifies distinct clinical outcomes and potential implications for immunotherapy

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Abstract

The epithelial-to-mesenchymal transition (EMT) is a reversible process that may interact with tumour immunity through multiple approaches. There is increasing evidence demonstrating the interconnections among EMT-related processes, the tumour microenvironment, and immune activity, as well as its potential influence on the immunotherapy response. Long non-coding RNAs (lncRNAs) are emerging as critical modulators of gene expression. They play fundamental roles in tumour immunity and act as promising biomarkers of immunotherapy response. However, the potential roles of lncRNA in the crosstalk of EMT and tumour immunity are still unclear in sarcoma. We obtained multi-omics profiling of 1440 pan-sarcoma patients from 19 datasets. Through an unsupervised consensus clustering approach, we categorised EMT molecular subtypes. We subsequently identified 26 EMT molecular subtype and tumour immune-related lncRNAs (EILncRNA) across pan-sarcoma types and developed an EILncRNA signature-based weighted scoring model (EILncSig). The EILncSig exhibited favourable performance in predicting the prognosis of sarcoma, and a high-EILncSig was associated with exclusive tumour microenvironment (TME) characteristics with desert-like infiltration of immune cells. Multiple altered pathways, somatically-mutated genes and recurrent CNV regions associated with EILncSig were identified. Notably, the EILncSig was associated with the efficacy of immune checkpoint inhibition (ICI) therapy. Using a computational drug-genomic approach, we identified compounds, such as Irinotecan that may have the potential to convert the EILncSig phenotype. By integrative analysis on multi-omics profiling, our findings provide a comprehensive resource for understanding the functional role of lncRNA-mediated immune regulation in sarcomas, which may advance the understanding of tumour immune response and the development of lncRNA-based immunotherapeutic strategies for sarcoma.

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Availability of data and materials

The accession IDs, web links for publicly available datasets analysed in this study are described in method section. All software and R packages used in our study are publicly available and denoted in the method section. Processed results of the present study are available in Supplementary files. R scripts and processed datasets for data analysis and visualisation are available on the GitHub (https://github.com/dyshi9/CMLS-D-22-00823).

Abbreviations

EMT:

Epithelial-to-mesenchymal transition

TME:

Tumour microenvironment

LncRNA:

Long non-coding RNAs

EILncRNA:

EMT and tumour Immune-related lncRNAs

EILncSig:

EILncRNA signature-based scoring model

CNV:

Copy number variation

ceRNA:

Competitive endogenous RNA

STS:

Soft tissue sarcomas

ICI:

Immune checkpoint inhibitor

UCA1:

Urothelial carcinoma-associated 1

PD1:

Programmed cell death 1

LIMIT:

LncRNA Inducing IFN-γ, MHC-I and Immunogenicity of tumour

MFS:

Metastasis-free survival

RFS:

Relapse-free survival

OS:

Overall survival

GSVA:

Gene set variation analysis

IL-10:

Interleukin-10

IFNG:

Interferon

TIME:

Tumour immune microenvironment

ROC:

Receiver operating characteristic

AUC:

Areas under the curve

TME subtypes-IE/F:

Immune-enriched, fibrotic form

TME subtypes-IE:

Immune-enriched, non-fibrotic form

TME subtypes-F:

Fibrotic form

TME subtypes-D:

Depleted form

FGES :

Functional gene expression signatures

sCNA:

Somatic copy number alteration

TNB:

Tumour neoantigen burden

MSI:

Microsatellite instability

CR/PR:

Complete/partial response

SD/PD:

Stable/progressed disease

P-Lipo:

PD-L1-targeting immune liposome

ICD:

Immunogenic cell death

PDL-1:

PD-ligand-1

GSEA:

Gene set enrichment analysis

NES:

Normalized enrichment score

GO:

Gene Ontology

KEGG:

Kyoto Encyclopaedia of Genes and Genomes

MSigDB:

Molecular Signatures Database

WGCNA:

Weighted gene co-expression network analysis

PCC:

Pearson's correlation coefficients

DEG:

Differentially-expressed genes

Sarcoma DDLPS:

Dedifferentiated liposarcoma

Sarcoma LMS:

Leiomyosarcoma

Sarcoma MFS:

Myxofibrosarcoma

Sarcoma SS:

Synovial sarcoma

Sarcoma UPS:

Undifferentiated pleomorphic sarcoma

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Acknowledgements

The results here are based on the data generated by the TCGA Research Network and the Therapeutically Applicable Research to Generate Effective Treatments. The study reported herein fully satisfies the TCGA and TARGET publication requirements (https://www.cancer.gov/tcga; https://ocg.cancer.gov/programs/target). The authors would like to thank the TCGA, TARGET and GEO developed by National Institutes of Health and the ArrayExpress developed by the European Bioinformatics Institute.

Funding

This work was supported by grants from the National Natural Science Foundation of China (grant No. 82072978 to JL, No. 82072979 to ZZ) and the Natural Science Foundation of Hubei Province (Grant No. 2020CFB861 to JL).

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Author notes

  1. Deyao Shi, Shidai Mu have contributed equally to this work.

Authors and Affiliations

  1. Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China

    Deyao Shi, Feifei Pu, Binlong Zhong, Binwu Hu, Muradil Muhtar, Wei Tong, Zengwu Shao, Zhicai Zhang & Jianxiang Liu

  2. Institute of Haematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China

    Shidai Mu

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  1. Deyao Shi
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Contributions

DS and SM contributed equally to this work. DS, SM, FP, BZ and BH collected data. DS, SM, FP, MM and WT analysed data and conducted statistical analysis. All authors contributed to data interpretation. DS, SM, FP, MM, WT, ZZ, ZS and JL drafted and revised the manuscript. DS, MS, MM and WT organized the R scripts and processed data. DS, ZZ and JL jointly conceived and supervised the study.

Corresponding authors

Correspondence to Deyao Shi, Zhicai Zhang or Jianxiang Liu.

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The patient cohorts we used were publicly available datasets that were collected with patients’ informed consent.

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Supplementary Information

Below is the link to the electronic supplementary material.

18_2022_4462_MOESM1_ESM.tif

Supplementary figure 1 Clustering analysis of pan-sarcoma EMT molecular subtypes. (a). Principle component analysis and datasets combination (before batch correction: left, after batch correction: right) via Combat function of the sva R package. (b). A stack plot showing overall TME-infiltering cells estimated by CIBERSORTx between EMT molecular subtypes. (c). Comparison of absolute scores of each TME-infiltering cell type estimated by CIBERSORTx. (d). Comparison of EMT scores between EMT molecular subtypes.(e). A stack plot showing the composition of sarcoma types between EMT molecular subtypes. (f). For each sarcoma type, proportions of patients with and without specific sarcoma between EMT molecular subtypes

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Supplementary figure 2 WGCNA for the identification of EMT molecular subtype related gene modules.(a). Preliminary sample clustering for the detection of outliers. (b). Sample dendrogram and trait heatmap of enrolled sample (not available information in grey). (c). Analysis of network topology for various soft-thresholding powers. Left: the scale-free fit index. Right: the mean connectivity. (d). A histogram and visual assessment of scale-free network topology. (e). Clustering of module eigengenes to quantify co-expression similarity of entire modules for modules merging. (f). Intramodular analysis on the correlation of module membership and gene significance for EMT molecular subtype (showing the five modules with relatively low correlation)

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Supplementary figure 3. Evaluation of EILncSig in aspects of EMT, immune subtypes and TME characteristics. (a). In the GSE71118 dataset, a stack plot showing the composition of sarcoma types and the proportions of patients with/without specific sarcoma types between EILncSig groups. (b). In the TCGA-SARC dataset, a stack plot showing the composition of sarcoma types and the proportions of patients with/without specific sarcoma types between EILncSig groups. (c). Comparison of EILncSig scores among various sarcoma types in the combined pan-sarcoma expression dataset. (d). ROC analysis and corresponding AUC for evaluating whether EILncSig score could predict certain sarcoma types. (e). Correlation of EILncSig scores and EMT scores. (f). Comparison of EILncSig scores between EMT molecular subtypes identified by consensus clustering. (g). Consensus matrix of the unsupervised consensus clustering on TME pattern in the combined sarcoma dataset. (h). Expression pattern of lncRNAs of the EILncSig among TCGA-SARC immune subtypes. (i). A stack plot showing overall TME-infiltering cells estimated by CIBERSORTx between EILncSig groups of TCGA-SARC

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Supplementary figure 4. Supplementary results of analysis of transcriptomic and genomic characteristics of sarcoma. (a). A heatmap showing DEGs expression of sarcoma patients between EILncSig groups of TCGA-SARC. (b). Somatic mutation landscape of TCGA-SARC patients. (c). Waterfall plot showing top mutated genes of TCGA-SARC patients (n=237). (d). Lollipop Plot showing mutation sites of TTN genes corresponding to high- and low-EILncSig groups. (e). Total CNV events (amplification and deletion) of TCGA-SARC patients. (f). No correlation between EILncSig scores and CNV amplification event

18_2022_4462_MOESM5_ESM.tif

Supplementary figure 5. Supplementary results of analysis of transcriptomic and genomic characteristics of sarcoma. Comparison of normalized expression of ICI genes between sarcoma patients of EILncSig groups (TIM-3, SIGLEC6, IDO1 and IDO2)

18_2022_4462_MOESM6_ESM.xlsx

Supplementary file 1. Sheet 1-3. Clinical information of patients included in the present study from 17 microarray datasets, TCGA-SARC and TARGET-OS RNA-Seq dataset. Sheet 4. Sample size for each dataset and overview of sarcoma histology subtypes. Sheet 5. The combined EMT signature.

18_2022_4462_MOESM7_ESM.xlsx

Supplementary file 2. Sheet 1. Result of consensus clustering for pan-sarcoma EMT molecular subtypes based on EMT signature expression pattern. Sheet 2-5. Comparisons of GSVA enrichment scores of patients in different EMT molecular subtypes. Sheet 6. Immune Score, Stromal Score and Microenvironment Score estimated by xCell.Sheet 7. Absolute score of each TME-infiltrating cell type estimated by CIBERSORTx. Sheet 8. EMT scores of patients of EMT molecular subtypes.

18_2022_4462_MOESM8_ESM.xlsx

Supplementary file 3. Sheet 1. Detailed WGCNA results. Sheet 2. The 72 lncRNAs identified to be associated to the EMT molecular subtypes. Sheet 3. Immune genes of Immport database with annotation. Sheet 4. GSVA scores of immune pathways. Sheet 5. Correlation test of lncRNA–immune pathway pairs. Sheet 6. Correlation test of lncRNA–immune genes pairs. Sheet 7. Correlation test of lncRNA–TME-infiltrating cell pairs. Sheet 8. The 37 robust lncRNA candidates involved in tumour immunity across pan-sarcoma type

18_2022_4462_MOESM9_ESM.xlsx

Supplementary file 4. Sheet 1. Gene list of EILncRNA. Sheet 2. Univariate and multivariate Cox regression analysis of EILncRNA on training cohort GSE71118. Sheet 3. EILncSig scores and stratification of EILncSig groups of training and validation cohorts. Sheet 4-5. Detailed clinical information use in the Cox regression analysis of EILncSig and clinical characteristics (TARGET-OS and TCGA-SARC). Sheet 6. Supporting clinical information and molecular subtype of TCGA-SARC obtained from Alexander et al.'s study (https://doi.org/10.1016/j.cell.2017.10.014). Sheet 7. Cox regression analysis results of EILncSig with clinicopathological characteristics in TARGET-OS and TCGA-SARC

18_2022_4462_MOESM10_ESM.xlsx

Supplementary file 5. Sheet 1. TME Signatures of Bagaev et al.’s study (https://doi.org/10.1016/j.ccell.2021.04.014). Sheet 2. Standardized GSVA enrichment scores of the 29 functional TME gene expression signatures. Sheet 3. Consensus clustering of TME pattern. Sheet 4. Supporting immune subtype of TCGA-SARC obtained from Thorsson et al.'s study (https://doi.org/10.1016/j.immuni.2018.03.023). Sheet 5. Absolute scores of TME-infiltrating cells estimated by CIBERSORTx (TCGA-SARC)

18_2022_4462_MOESM11_ESM.xlsx

Supplementary file 6. Sheet 1. DEG analysis result of patients in high- and low-EILncSig groups. Sheet 2-3. GESA result based on the gene sets of GOBP and REACTOME pathways. Sheet 4. Putative copy-number variation events from GISTIC 2.0 (TCGA-SARC). Sheet 5-6. Identification of genes within recurrent CNV regions

18_2022_4462_MOESM12_ESM.xlsx

Supplementary file 7. Sheet 1. EILncSig and clinical information of an ICI therapy cohort GSE176307. Sheet 2. Connectivity scores and significance test for drug prediction based on CMAP database

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Shi, D., Mu, S., Pu, F. et al. Pan-sarcoma characterization of lncRNAs in the crosstalk of EMT and tumour immunity identifies distinct clinical outcomes and potential implications for immunotherapy. Cell. Mol. Life Sci. 79, 427 (2022). https://doi.org/10.1007/s00018-022-04462-4

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