Single-Cell Transcriptomics of Immune Cells Reveal Diversity and Exhaustion Signatures in Non-Small-Cell Lung Cancer

Zhao, Ying; Zhang, Qilin; Tu, Kailin; Chen, Yanmei; Peng, Yuxuan; Ni, Yingmeng; Zhu, Guonian; Cheng, Cheng; Li, Yangqian; Xiao, Xue; Yu, Chunyan; Lu, Keying; Chen, Yaxin; Li, Chengpin; Tang, Jun; Wang, Gang; Luo, Wenxin; Zhang, Wengeng; Che, Guowei; Liu, Weimin; Wang, Zhoufeng; Xie, Dan

doi:10.3389/fimmu.2022.854724

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…TEX-related genes can be extracted from CD8TEX cells from the scRNA-seq database. Additionally, TEX-based gene signatures have been widely used to predict prognostic, molecular, and immune features, as well as therapeutic responses in several tumors, including LIHC, lung adenocarcinoma, and NSCLC [ 34 , 35 , 36 , 37 ]. In this study, we identified and validated a TEX-related signature for predicting the prognosis and immune response in PACA using integrated analysis of scRNA-seq and bulk RNA sequencing data.…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data

Zhu,

Tan,

Luo

et al. 2024

Diagnostics

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Purpose: Pancreatic cancer (PACA) is one of the most fatal malignancies worldwide. Immunotherapy is largely ineffective in patients with PACA. T-cell exhaustion contributes to immunotherapy resistance. We investigated the prognostic potential of T-cell exhaustion-related genes (TEXGs). Methods: A single-cell RNA (scRNA) sequencing dataset from Tumor Immune Single-Cell Hub (TISCH) and bulk sequencing datasets from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to screen differentially expressed TEXGs. Kaplan–Meier survival, LASSO regression, and univariate/multivariate Cox regression analyses were performed to construct a TEXG risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. The PACA cohorts from the ICGC and GSE71729 datasets were used to validate the risk model. Pan-cancer expression of SPOCK2 was determined using the TISCH database. Results: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Patients with low risk had prolonged survival times in both the training (TCGA-PAAD, n = 178) and validation (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n = 412) datasets. Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PACA. High-risk patients correlated with their immunosuppressive status. Immunohistochemical staining confirmed the changes in TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 may be a novel marker of exhausted CD8+ T-cells. Conclusion: We established and validated a T-cell exhaustion-related prognostic signature for patients with PACA. Moreover, our study suggests that SPOCK2 is a novel marker of exhausted CD8+ T cells.

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Section: Discussionmentioning

confidence: 99%

Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data

Zhu,

Tan,

Luo

et al. 2024

Diagnostics

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Transcription Factor ETV4 Activates AURKA to Promote PD-L1 Expression and Mediate Immune Escape in Lung Adenocarcinoma

Yang,

He,

et al. 2024

Int Arch Allergy Immunol

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Introduction: The occurrence and progression of lung adenocarcinoma (LUAD) impair T-cell immune responses, causing immune escape and subsequently affecting the efficacy of immunotherapy in patients. Aurora kinase A (AURKA) is upregulated in varying cancers, but its role in LUAD immune escape is elusive. This work attempted to explore molecular mechanisms of AURKA regulation in LUAD immune escape. Methods: Through bioinformatics analysis, AURKA level in LUAD was evaluated, and potential upstream transcription factors of AURKA were predicted using hTFtarget. ETS variant transcription factor 4 (ETV4) expression in LUAD was analyzed through The Cancer Genome Atlas. Pearson’s correlation analysis was then utilized to test the correlation between AURKA and ETV4. Interaction and binding between AURKA and ETV4 were validated through dual-luciferase assay and chromatin immunoprecipitation. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) tested relative mRNA expression of AURKA and ETV4 in LUAD cells, cell counting kit-8 assayed cell viability, and Western blot analysis was conducted to determine the protein level of programmed death-ligand 1 (PD-L1). Coculture of LUAD cells with activated CD8+ T cells was carried out, and an LDH assay was used to assess the cytotoxicity of CD8+ T cells against LUAD cells. Interferon-γ (IFN-γ), interleukin-2 (IL-2), and tumor necrosis factor-α (TNF-α) levels in the coculture system were assessed by enzyme-linked immunosorbent assay (ELISA). Western blot assessed protein levels of JAK2, p-JAK2, STAT3, and p-STAT3. Results: Compared to normal tissues, AURKA and ETV4 were upregulated in tumor tissues, and AURKA presented a negative association with CD8+ T-cell immune infiltration but a positive association with PD-L1. qRT-PCR unveiled significantly upregulated mRNA of AURKA and ETV4 in LUAD cells compared to normal lung epithelial cells. Knockdown of AURKA significantly decreased cell viability and PD-L1 protein level in LUAD cells, enhanced cytotoxicity of CD8+ T cells against LUAD cells and IFN-γ, IL-2, and TNF-α expression, while overexpression of AURKA yielded opposite results. Furthermore, the knockdown of ETV4 could reverse the oncogenic characteristics of cells caused by AURKA overexpression. Conclusion: Our study illustrated that ETV4/AURKA axis promoted PD-L1 expression, suppressed CD8+ T-cell activity, and mediated immune escape in LUAD by regulating the JAK2/STAT3 signaling pathway.

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Single cell RNA sequencing provides novel cellular transcriptional profiles and underlying pathogenesis of presbycusis

Zhang,

Xiang,

Sun

et al. 2024

BMC Med Genomics

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Age-related hearing loss (ARHL) or presbycusis is associated with irreversible progressive damage in the inner ear, where the sound is transduced into electrical signal; but the detailed mechanism remains unclear. Here, we sought to determine the potential molecular mechanism involved in the pathogeneses of ARHL with bioinformatics methods. A single-cell transcriptome sequencing study was performed on the cochlear samples from young and aged mice. Detection of identified cell type marker allowed us to screen 18 transcriptional clusters, including myeloid cells, epithelial cells, B cells, endothelial cells, fibroblasts, T cells, inner pillar cells, neurons, inner phalangeal cells, and red blood cells. Cell-cell communications were analyzed between young and aged cochlear tissue samples by using the latest integration algorithms Cellchat. A total of 56 differentially expressed genes were screened between the two groups. Functional enrichment analysis showed these genes were mainly involved in immune, oxidative stress, apoptosis, and metabolic processes. The expression levels of crucial genes in cochlear tissues were further verified by immunohistochemistry. Overall, this study provides new theoretical support for the development of clinical therapeutic drugs. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-024-02001-7.

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Single-Cell Transcriptomics of Immune Cells Reveal Diversity and Exhaustion Signatures in Non-Small-Cell Lung Cancer

Cited by 4 publications

References 42 publications

Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data

Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data

Transcription Factor ETV4 Activates AURKA to Promote PD-L1 Expression and Mediate Immune Escape in Lung Adenocarcinoma

Single cell RNA sequencing provides novel cellular transcriptional profiles and underlying pathogenesis of presbycusis

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