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The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data

BACKGROUND: Neoadjuvant chemotherapy (NACT) becomes the first-line option for advanced tumors, while patients who are not sensitive to it may not benefit. Therefore, it is important to screen patients suitable for NACT. METHODS: Single-cell data of lung adenocarcinoma (LUAD) and esophageal squamous...

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Autores principales: Sui, Qihai, Hu, Zhengyang, Jin, Xing, Bian, Yunyi, Liang, Jiaqi, Zhang, Huan, Yang, Huiqiang, Lin, Zongwu, Wang, Qun, Zhan, Cheng, Chen, Zhencong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249226/
https://www.ncbi.nlm.nih.gov/pubmed/37291676
http://dx.doi.org/10.1186/s13578-023-01061-z
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author Sui, Qihai
Hu, Zhengyang
Jin, Xing
Bian, Yunyi
Liang, Jiaqi
Zhang, Huan
Yang, Huiqiang
Lin, Zongwu
Wang, Qun
Zhan, Cheng
Chen, Zhencong
author_facet Sui, Qihai
Hu, Zhengyang
Jin, Xing
Bian, Yunyi
Liang, Jiaqi
Zhang, Huan
Yang, Huiqiang
Lin, Zongwu
Wang, Qun
Zhan, Cheng
Chen, Zhencong
author_sort Sui, Qihai
collection PubMed
description BACKGROUND: Neoadjuvant chemotherapy (NACT) becomes the first-line option for advanced tumors, while patients who are not sensitive to it may not benefit. Therefore, it is important to screen patients suitable for NACT. METHODS: Single-cell data of lung adenocarcinoma (LUAD) and esophageal squamous carcinoma (ESCC) before and after cisplatin-containing (CDDP) NACT and cisplatin IC50 data of tumor cell lines were analyzed to establish a CDDP neoadjuvant chemotherapy score (NCS). Differential analysis, GO, KEGG, GSVA and logistic regression models were performed by R. Survival analysis were applied to public databases. siRNA knockdown in A549, PC9, TE1 cell lines, qRT-PCR, western-blot, cck8 and EdU experiments were used for further verification in vitro. RESULTS: 485 genes were expressed differentially in tumor cells before and after neoadjuvant treatment for LUAD and ESCC. After combining the CDDP-associated genes, 12 genes, CAV2, PHLDA1, DUSP23, VDAC3, DSG2, SPINT2, SPATS2L, IGFBP3, CD9, ALCAM, PRSS23, PERP, were obtained and formed the NCS score. The higher the score, the more sensitive the patients were to CDDP-NACT. The NCS divided LUAD and ESCC into two groups. Based on differentially expressed genes, a model was constructed to predict the high and low NCS. CAV2, PHLDA1, ALCAM, CD9, IGBP3 and VDAC3 were significantly associated with prognosis. Finally, we demonstrated that the knockdown of CAV2, PHLDA1 and VDAC3 in A549, PC9 and TE1 significantly increased the sensitivity to cisplatin. CONCLUSIONS: NCS scores and related predictive models for CDDP-NACT were developed and validated to assist in selecting patients who might benefit from it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13578-023-01061-z.
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spelling pubmed-102492262023-06-09 The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data Sui, Qihai Hu, Zhengyang Jin, Xing Bian, Yunyi Liang, Jiaqi Zhang, Huan Yang, Huiqiang Lin, Zongwu Wang, Qun Zhan, Cheng Chen, Zhencong Cell Biosci Research BACKGROUND: Neoadjuvant chemotherapy (NACT) becomes the first-line option for advanced tumors, while patients who are not sensitive to it may not benefit. Therefore, it is important to screen patients suitable for NACT. METHODS: Single-cell data of lung adenocarcinoma (LUAD) and esophageal squamous carcinoma (ESCC) before and after cisplatin-containing (CDDP) NACT and cisplatin IC50 data of tumor cell lines were analyzed to establish a CDDP neoadjuvant chemotherapy score (NCS). Differential analysis, GO, KEGG, GSVA and logistic regression models were performed by R. Survival analysis were applied to public databases. siRNA knockdown in A549, PC9, TE1 cell lines, qRT-PCR, western-blot, cck8 and EdU experiments were used for further verification in vitro. RESULTS: 485 genes were expressed differentially in tumor cells before and after neoadjuvant treatment for LUAD and ESCC. After combining the CDDP-associated genes, 12 genes, CAV2, PHLDA1, DUSP23, VDAC3, DSG2, SPINT2, SPATS2L, IGFBP3, CD9, ALCAM, PRSS23, PERP, were obtained and formed the NCS score. The higher the score, the more sensitive the patients were to CDDP-NACT. The NCS divided LUAD and ESCC into two groups. Based on differentially expressed genes, a model was constructed to predict the high and low NCS. CAV2, PHLDA1, ALCAM, CD9, IGBP3 and VDAC3 were significantly associated with prognosis. Finally, we demonstrated that the knockdown of CAV2, PHLDA1 and VDAC3 in A549, PC9 and TE1 significantly increased the sensitivity to cisplatin. CONCLUSIONS: NCS scores and related predictive models for CDDP-NACT were developed and validated to assist in selecting patients who might benefit from it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13578-023-01061-z. BioMed Central 2023-06-08 /pmc/articles/PMC10249226/ /pubmed/37291676 http://dx.doi.org/10.1186/s13578-023-01061-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sui, Qihai
Hu, Zhengyang
Jin, Xing
Bian, Yunyi
Liang, Jiaqi
Zhang, Huan
Yang, Huiqiang
Lin, Zongwu
Wang, Qun
Zhan, Cheng
Chen, Zhencong
The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
title The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
title_full The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
title_fullStr The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
title_full_unstemmed The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
title_short The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
title_sort genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249226/
https://www.ncbi.nlm.nih.gov/pubmed/37291676
http://dx.doi.org/10.1186/s13578-023-01061-z
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