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Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies
Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

Background: Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information. Methods: We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the “movics” R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model. Results: CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD. Conclusions: Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

Lung cancer, multi-omics profiles, precision medicine, prognosis model, tumor microenvironment
2218-6751
2243-2260
Zou, Yanmei
158bb794-746d-4b10-8b69-9929559b2682
Cao, Chenlin
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Wang, Yali
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Zhou, Yilu
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Yao, Shuo
b30d1beb-86f9-472d-aa90-c1407dbd8a0a
Zhang, Lili
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Zheng, Kun
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Zhang, Hong
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Qin, Wan
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Qin, Kai
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Xiong, Huihua
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Yuan, Xianglin
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Fu, Shengling
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Wang, Yihua
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Xiong, Hua
01fbf072-17a0-47fa-8af5-f4b8cfcd65d5
Zou, Yanmei
158bb794-746d-4b10-8b69-9929559b2682
Cao, Chenlin
502aed1f-4c15-4126-a01a-98bb3b07ecb3
Wang, Yali
d230ac5a-ed19-42bb-96cd-2a29428fc38d
Zhou, Yilu
1878565d-39e6-467d-a027-7320bf4cdaf2
Yao, Shuo
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Zhang, Lili
45b1b29a-873f-43b7-a6c7-13dbc268f08d
Zheng, Kun
0fda8b5d-76e1-4e6b-ba70-142823ad94d2
Zhang, Hong
6b9edf9a-72bf-4c38-9e47-f5d7d9ec6971
Qin, Wan
07f2cd6b-1380-4378-82a2-e54959d6ca83
Qin, Kai
9203e104-cb9d-4d6b-8110-4164f8721168
Xiong, Huihua
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Yuan, Xianglin
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Fu, Shengling
9c3fe341-225a-4936-a31c-b7dba8fa323c
Wang, Yihua
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Xiong, Hua
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Zou, Yanmei, Cao, Chenlin, Wang, Yali, Zhou, Yilu, Yao, Shuo, Zhang, Lili, Zheng, Kun, Zhang, Hong, Qin, Wan, Qin, Kai, Xiong, Huihua, Yuan, Xianglin, Fu, Shengling, Wang, Yihua and Xiong, Hua (2022) Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies. Translational Lung Cancer Research, 11 (11), 2243-2260. (doi:10.21037/tlcr-22-775).

Record type: Article

Abstract

Background: Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information. Methods: We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the “movics” R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model. Results: CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD. Conclusions: Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

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Accepted/In Press date: 21 November 2022
Published date: 29 November 2022
Additional Information: Funding Information: Funding: This project was supported by the Natural Science Foundation of Hubei Province (No. 2021CFB372, to Hua Xiong) and the National Natural Science Foundation of China (No. 82272902, to Yanmei Zou). Publisher Copyright: © Translational Lung Cancer Research. All rights reserved.
Keywords: Lung cancer, multi-omics profiles, precision medicine, prognosis model, tumor microenvironment

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Local EPrints ID: 473437
URI: http://eprints.soton.ac.uk/id/eprint/473437
ISSN: 2218-6751
PURE UUID: 0ca47346-c71e-46db-9072-67a1bfa11793
ORCID for Yilu Zhou: ORCID iD orcid.org/0000-0002-4090-099X
ORCID for Yihua Wang: ORCID iD orcid.org/0000-0001-5561-0648

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Date deposited: 18 Jan 2023 17:41
Last modified: 17 Mar 2024 03:39

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Contributors

Author: Yanmei Zou
Author: Chenlin Cao
Author: Yali Wang
Author: Yilu Zhou ORCID iD
Author: Shuo Yao
Author: Lili Zhang
Author: Kun Zheng
Author: Hong Zhang
Author: Wan Qin
Author: Kai Qin
Author: Huihua Xiong
Author: Xianglin Yuan
Author: Shengling Fu
Author: Yihua Wang ORCID iD
Author: Hua Xiong

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