Cargando…

Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma

BACKGROUND: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). METHODS: We first used histopathological image features and machine‐learning algorithms to pred...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Linyan, Zeng, Hao, Zhang, Mingxuan, Luo, Yuling, Ma, Xuelei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267162/
https://www.ncbi.nlm.nih.gov/pubmed/33987946
http://dx.doi.org/10.1002/cam4.3965
_version_ 1783720087820173312
author Chen, Linyan
Zeng, Hao
Zhang, Mingxuan
Luo, Yuling
Ma, Xuelei
author_facet Chen, Linyan
Zeng, Hao
Zhang, Mingxuan
Luo, Yuling
Ma, Xuelei
author_sort Chen, Linyan
collection PubMed
description BACKGROUND: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). METHODS: We first used histopathological image features and machine‐learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA‐HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. RESULTS: Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5‐year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5‐year AUC = 0.860) and test set (5‐year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high‐risk and low‐risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. CONCLUSIONS: Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC.
format Online
Article
Text
id pubmed-8267162
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-82671622021-07-13 Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma Chen, Linyan Zeng, Hao Zhang, Mingxuan Luo, Yuling Ma, Xuelei Cancer Med Bioinformatics BACKGROUND: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). METHODS: We first used histopathological image features and machine‐learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA‐HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. RESULTS: Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5‐year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5‐year AUC = 0.860) and test set (5‐year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high‐risk and low‐risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. CONCLUSIONS: Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC. John Wiley and Sons Inc. 2021-05-13 /pmc/articles/PMC8267162/ /pubmed/33987946 http://dx.doi.org/10.1002/cam4.3965 Text en © 2021 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 Bioinformatics
Chen, Linyan
Zeng, Hao
Zhang, Mingxuan
Luo, Yuling
Ma, Xuelei
Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
title Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
title_full Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
title_fullStr Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
title_full_unstemmed Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
title_short Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
title_sort histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267162/
https://www.ncbi.nlm.nih.gov/pubmed/33987946
http://dx.doi.org/10.1002/cam4.3965
work_keys_str_mv AT chenlinyan histopathologicalimageandgeneexpressionpatternanalysisforpredictingmolecularfeaturesandprognosisofheadandnecksquamouscellcarcinoma
AT zenghao histopathologicalimageandgeneexpressionpatternanalysisforpredictingmolecularfeaturesandprognosisofheadandnecksquamouscellcarcinoma
AT zhangmingxuan histopathologicalimageandgeneexpressionpatternanalysisforpredictingmolecularfeaturesandprognosisofheadandnecksquamouscellcarcinoma
AT luoyuling histopathologicalimageandgeneexpressionpatternanalysisforpredictingmolecularfeaturesandprognosisofheadandnecksquamouscellcarcinoma
AT maxuelei histopathologicalimageandgeneexpressionpatternanalysisforpredictingmolecularfeaturesandprognosisofheadandnecksquamouscellcarcinoma