Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Wang, Zhenling; Yu, Shanshan; Zhang, Hongqiang; Lu, Yao; Chen, Yang; Shen, Hengyang; Huang, Changzhi; Wu, Jingyu; Fu, Zan

doi:10.3389/fimmu.2023.1181985

Cited by 4 publications

(3 citation statements)

References 79 publications

(86 reference statements)

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“…We also evaluated the immunotherapeutic response of cluster I and cluster II using the IMvigor210 and GSE78220 datasets, as done in previous studies ( 29 , 30 ). Although we identified an association between the ARC and the TME, our analysis did not reveal a significant correlation between these subtypes and immunotherapy response or patient survival.…”

Section: Discussionmentioning

confidence: 99%

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Wei,

Xie,

Liu

et al. 2024

Front. Oncol.

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BackgroundAngiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis.MethodsCRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay.ResultsTwo distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls.ConclusionThis study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Wei,

Xie,

Liu

et al. 2024

Front. Oncol.

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“…To find the best features without using GEPs, DL techniques can be trained on the entire transcriptome or dataset. MethylNet (recently developed DL TME technique) additionally integrated the SHAP explainability technique to measure the significance of every CpG site for deconvolution [82], [83].…”

Section: Precision Oncologymentioning

confidence: 99%

Role of Deep Learning in Diagnosis, Treatment, and Prognosis of Oncological Conditions

Umar,

Shiwlani,

Saeed

et al. 2023

ijmst

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Deep learning, a branch of artificial intelligence, excavates massive data sets for patterns and predictions using a machine learning method known as artificial neural networks. Research on the potential applications of deep learning in understanding the intricate biology of cancer has intensified due to its increasing applications among healthcare domains and the accessibility of extensively characterized cancer datasets. Although preliminary findings are encouraging, this is a fast-moving sector where novel insights into deep learning and cancer biology are being discovered. We give a framework for new deep learning methods and their applications in oncology in this review. Our attention was directed towards its applications for DNA methylation, transcriptomic, and genomic data, along with histopathological inferences. We offer insights into how these disparate data sets can be combined for the creation of decision support systems. Specific instances of learning applications in cancer prognosis, diagnosis, and therapy planning are presented. Additionally, the present barriers and difficulties in deep learning applications in the field of precision oncology, such as the dearth of phenotypical data and the requirement for more explicable deep learning techniques have been elaborated. We wrap up by talking about ways to get beyond the existing challenges so that deep learning can be used in healthcare settings in the future.

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“…Exploration of four ARC-related genes at scRNA-seq level. We also evaluated the immunotherapeutic response of cluster I and cluster II using the IMvigor210 and GSE78220 datasets, as done in previous studies (29,30). Although we identified an association between the ARC and the TME, our analysis did not reveal a significant correlation between these subtypes and immunotherapy response or patient survival.…”

Section: B C Amentioning

confidence: 77%

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Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Cited by 4 publications

References 79 publications

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Role of Deep Learning in Diagnosis, Treatment, and Prognosis of Oncological Conditions

Table_1.docx

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