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Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma

BACKGROUND: Single‐cell transcriptomics has been used to investigate various tumors to elucidate the molecular distinction of all cell type compositions of a complex mix. AIMS: This study aimed to investigate malignant‐cell‐specific genes to explore diagnostic and therapeutic biomarkers using single...

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Autores principales: Liang, Jiaqi, Chen, Zhencong, Huang, Yiwei, Bi, Guoshu, Bian, Yunyi, Jin, Xing, Zhang, Huan, Sui, Qihai, Zhan, Cheng, Wang, Qun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9160812/
https://www.ncbi.nlm.nih.gov/pubmed/35102706
http://dx.doi.org/10.1002/cam4.4547
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author Liang, Jiaqi
Chen, Zhencong
Huang, Yiwei
Bi, Guoshu
Bian, Yunyi
Jin, Xing
Zhang, Huan
Sui, Qihai
Zhan, Cheng
Wang, Qun
author_facet Liang, Jiaqi
Chen, Zhencong
Huang, Yiwei
Bi, Guoshu
Bian, Yunyi
Jin, Xing
Zhang, Huan
Sui, Qihai
Zhan, Cheng
Wang, Qun
author_sort Liang, Jiaqi
collection PubMed
description BACKGROUND: Single‐cell transcriptomics has been used to investigate various tumors to elucidate the molecular distinction of all cell type compositions of a complex mix. AIMS: This study aimed to investigate malignant‐cell‐specific genes to explore diagnostic and therapeutic biomarkers using single‐cell transcriptomic data of lung adenocarcinoma. MATERIALS & METHODS: 10X single‐cell RNA‐seq data of fourteen patients with lung adenocarcinoma were analyzed. Genes that expressed differentially and those with higher confidence to distinguish tumor cells from normal cells were picked out using the ROC curves. The LASSO regression method was used to select most markedly correlated genes to predict the malignancy of every single cell within a model. We also conducted further experiments to determine their roles in lung cancer in vitro. RESULTS: Twenty two thousand four hundred and ninety one tumor and 181 666 normal single cells were analyzed where 369 genes were found to be specifically expressed in single malignant cells. Seventy of them, encoding secreted or membrane‐bound proteins, showed involvement in cell‐to‐cell communications in tumor biology. KRT18 and the other six genes were identified as predictors to distinguish single malignant cells and were integrated to construct an accurate (96.1%) predicting model. Notably, IRX2, SPINK13, and CAPN8 outperformed the other four genes. Further experiments confirmed the upregulation of them in lung adenocarcinoma at both tissue and cell levels. Proliferative capacities of lung adenocarcinoma cells were attenuated by knocking‐down of either of them. However, targeting CAPN8, IRX2, or SPINK13 hardly exerted a cytotoxic effect on these cells. DISCUSSION: Apart from the current model, similar tools were still warranted using single‐cell RNA‐seq data of more types of tumors. The three genes identified as potential therapeutic targets in the present study still need to be validated in more in lung cancer. CONCLUSION: Our model can aid the analyses of single‐cell sequencing data. CAPN8, IRX2, and SPINK13 may serve as novel targets of targeted and immune‐based therapies in lung adenocarcinoma.
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spelling pubmed-91608122022-06-04 Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma Liang, Jiaqi Chen, Zhencong Huang, Yiwei Bi, Guoshu Bian, Yunyi Jin, Xing Zhang, Huan Sui, Qihai Zhan, Cheng Wang, Qun Cancer Med RESEARCH ARTICLES BACKGROUND: Single‐cell transcriptomics has been used to investigate various tumors to elucidate the molecular distinction of all cell type compositions of a complex mix. AIMS: This study aimed to investigate malignant‐cell‐specific genes to explore diagnostic and therapeutic biomarkers using single‐cell transcriptomic data of lung adenocarcinoma. MATERIALS & METHODS: 10X single‐cell RNA‐seq data of fourteen patients with lung adenocarcinoma were analyzed. Genes that expressed differentially and those with higher confidence to distinguish tumor cells from normal cells were picked out using the ROC curves. The LASSO regression method was used to select most markedly correlated genes to predict the malignancy of every single cell within a model. We also conducted further experiments to determine their roles in lung cancer in vitro. RESULTS: Twenty two thousand four hundred and ninety one tumor and 181 666 normal single cells were analyzed where 369 genes were found to be specifically expressed in single malignant cells. Seventy of them, encoding secreted or membrane‐bound proteins, showed involvement in cell‐to‐cell communications in tumor biology. KRT18 and the other six genes were identified as predictors to distinguish single malignant cells and were integrated to construct an accurate (96.1%) predicting model. Notably, IRX2, SPINK13, and CAPN8 outperformed the other four genes. Further experiments confirmed the upregulation of them in lung adenocarcinoma at both tissue and cell levels. Proliferative capacities of lung adenocarcinoma cells were attenuated by knocking‐down of either of them. However, targeting CAPN8, IRX2, or SPINK13 hardly exerted a cytotoxic effect on these cells. DISCUSSION: Apart from the current model, similar tools were still warranted using single‐cell RNA‐seq data of more types of tumors. The three genes identified as potential therapeutic targets in the present study still need to be validated in more in lung cancer. CONCLUSION: Our model can aid the analyses of single‐cell sequencing data. CAPN8, IRX2, and SPINK13 may serve as novel targets of targeted and immune‐based therapies in lung adenocarcinoma. Blackwell Publishing Ltd 2022-01-31 /pmc/articles/PMC9160812/ /pubmed/35102706 http://dx.doi.org/10.1002/cam4.4547 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RESEARCH ARTICLES
Liang, Jiaqi
Chen, Zhencong
Huang, Yiwei
Bi, Guoshu
Bian, Yunyi
Jin, Xing
Zhang, Huan
Sui, Qihai
Zhan, Cheng
Wang, Qun
Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
title Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
title_full Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
title_fullStr Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
title_full_unstemmed Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
title_short Signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
title_sort signatures of malignant cells and novel therapeutic targets revealed by single‐cell sequencing in lung adenocarcinoma
topic RESEARCH ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9160812/
https://www.ncbi.nlm.nih.gov/pubmed/35102706
http://dx.doi.org/10.1002/cam4.4547
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