Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma
BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-09-01
|
Series: | Frontiers in Oncology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.636451/full |
_version_ | 1818913139658326016 |
---|---|
author | Hui Li Hui Li Linyan Chen Linyan Chen Hao Zeng Qimeng Liao Qimeng Liao Jianrui Ji Jianrui Ji Xuelei Ma Xuelei Ma |
author_facet | Hui Li Hui Li Linyan Chen Linyan Chen Hao Zeng Qimeng Liao Qimeng Liao Jianrui Ji Jianrui Ji Xuelei Ma Xuelei Ma |
author_sort | Hui Li |
collection | DOAJ |
description | BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future. |
first_indexed | 2024-12-19T23:25:45Z |
format | Article |
id | doaj.art-86b583cc2ef449938a51034284782abc |
institution | Directory Open Access Journal |
issn | 2234-943X |
language | English |
last_indexed | 2024-12-19T23:25:45Z |
publishDate | 2021-09-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Oncology |
spelling | doaj.art-86b583cc2ef449938a51034284782abc2022-12-21T20:01:51ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-09-011110.3389/fonc.2021.636451636451Integrative Analysis of Histopathological Images and Genomic Data in Colon AdenocarcinomaHui Li0Hui Li1Linyan Chen2Linyan Chen3Hao Zeng4Qimeng Liao5Qimeng Liao6Jianrui Ji7Jianrui Ji8Xuelei Ma9Xuelei Ma10Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaWest China Hospital, West China School of Medicine, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaWest China Hospital, West China School of Medicine, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaWest China Hospital, West China School of Medicine, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaWest China Hospital, West China School of Medicine, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaWest China Hospital, West China School of Medicine, Sichuan University, Chengdu, ChinaBackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.https://www.frontiersin.org/articles/10.3389/fonc.2021.636451/fullcolon adenocarcinomahistopathological featuresgenomic datarandom forestprognosis |
spellingShingle | Hui Li Hui Li Linyan Chen Linyan Chen Hao Zeng Qimeng Liao Qimeng Liao Jianrui Ji Jianrui Ji Xuelei Ma Xuelei Ma Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma Frontiers in Oncology colon adenocarcinoma histopathological features genomic data random forest prognosis |
title | Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma |
title_full | Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma |
title_fullStr | Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma |
title_full_unstemmed | Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma |
title_short | Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma |
title_sort | integrative analysis of histopathological images and genomic data in colon adenocarcinoma |
topic | colon adenocarcinoma histopathological features genomic data random forest prognosis |
url | https://www.frontiersin.org/articles/10.3389/fonc.2021.636451/full |
work_keys_str_mv | AT huili integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT huili integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT linyanchen integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT linyanchen integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT haozeng integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT qimengliao integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT qimengliao integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT jianruiji integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT jianruiji integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT xueleima integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma AT xueleima integrativeanalysisofhistopathologicalimagesandgenomicdataincolonadenocarcinoma |