A 13-immune gene set signature for prediction of colon cancer prognosis

Liu, Zhuo; Chen, Jin; Yan, Jiongyi; LIU, Qiaoming; Li, Fang; Xiong, Wanna; Lin, Shida; Yu, Kai; JingZhen, Liang

doi:10.2174/1386207323666200930104744

Cited by 3 publications

(2 citation statements)

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“…Notably, high-risk samples exhibited signs of immunosuppression. Furthermore, the riskScore derived from this study outperformed predictions made by previously published models [39].…”

Section: Lu Et Al (2021) [39]supporting

confidence: 56%

A Systematic Review of Machine Learning Prediction Models for Colorectal Cancer Patient Survival Using Clinical Data and Gene Expression Profiles

Onuiri,

Akande,

Kalesanwo

et al. 2023

RIA

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Colorectal cancer persistently ranks among the top causes of cancer-related mortality globally. The development of superior predictive methodologies is imperative for augmenting survival outcomes. This systematic review, conducted in accordance with PRISMA-P guidelines, scrutinizes studies carried out between 2013 and 2023 that apply machine learning models to prognosticate survival in colorectal cancer patients, particularly those models incorporating clinical data and gene expression profiles. Criteria for inclusion comprised studies employing machine learning techniques, with specific emphasis on those integrating clinical data and gene expression profiles for predictive purposes. Studies devoid of explicit methodological delineation or not written in English were excluded. Decision trees, neural networks, and support vector machines emerged as the most frequently scrutinized models in the review. While some models manifested high accuracy, others underscored areas requiring refinement. Predominant data sources included patient clinical records, gene expression datasets, and molecular profiling. The results underscore the potential of machine learning in bolstering predictive precision, thereby implicating a trajectory for future research targeting the optimization of patient prognosis and treatment outcomes in colorectal cancer.

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“…Notably, high-risk samples exhibited signs of immunosuppression. Furthermore, the riskScore derived from this study outperformed predictions made by previously published models [39].…”

Section: Lu Et Al (2021) [39]supporting

confidence: 56%

A Systematic Review of Machine Learning Prediction Models for Colorectal Cancer Patient Survival Using Clinical Data and Gene Expression Profiles

Onuiri,

Akande,

Kalesanwo

et al. 2023

RIA

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“…In recent years, several immune-related gene signatures have been identified and used for the prognosis prediction of colon cancer ( 52 ). For instance, Li et al constructed a ten-gene immune-related signature that reflected the immune microenvironment and survival rates in colon cancer ( 53 ).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of the correlation between GSTM1 and tumor immunity in colon cancer

Luo¹,

Tang²,

Ling³

et al. 2022

J Gastrointest Oncol

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Background: Glutathione S-transferase mu 1 (GSTM1) is one of the major glutathione conjugation enzymes. Its expression and activity have been suggested to correlate with the occurrence of colon cancer; however, the role of GSTM1 in tumor immunity remains unclear.Methods: Relevant data downloaded from The Cancer Genome Atlas (TGGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) was used to perform a multi-dimensional expression analysis of GSTM1 in colon adenocarcinoma (COAD). The correlation between GSTM1 and tumor immunity was analyzed with multiple online tools. Then protein-protein interaction (PPI) network and functional enrichment analyses of GSTM1-associated immunomodulators were performed. Further, we developed the Cox regression model based on the GSTM1-related immunomodulators. Finally, a GSTM1based clinical nomogram and a calibration curve was established to predict the probability and accuracy of long-term survival.Result: GSTM1 was significantly downregulated in COAD versus normal tissues. Infiltration levels of B cells, CD8 + T cells, and dendritic cells were closely correlated to GSTM1 gene copy number deletion, and GSTM1 expression levels in COAD positively correlated with dendritic cell, B cell, neutrophil, and macrophage infiltration. Functional enrichment analysis indicated 36 GSTM1-related immunomodulators are involved in immune-related pathways of regulating T cell activation and lymphocytic activation. A 2-gene prognostic risk signature based on the 36 GSTM1-related immunomodulators was built using the Cox regression model, and the risk signature in combination with stage had an area under the curve (AUC) value of 0.747 by the receiver operating characteristic method. patients with higher risk scores-calculated based on 2 gene prognostic risk characteristics and further identified as an independent prognostic factorwere associated with worse survival using the Kaplan-Meier analysis. Together, the clinical nomogram and calibration curve based on GSTM1 suggested a good prediction accuracy for long-term survival probability.Conclusions: Our study provided evidence supporting the significant role of GSTM1 in COAD immunity and suggests GSTM1 as a potential novel target for COAD immunotherapy.

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Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Feng

Disis

Cheng

et al. 2022

Laboratory Investigation

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A 13-immune gene set signature for prediction of colon cancer prognosis

Cited by 3 publications

References 0 publications

A Systematic Review of Machine Learning Prediction Models for Colorectal Cancer Patient Survival Using Clinical Data and Gene Expression Profiles

A Systematic Review of Machine Learning Prediction Models for Colorectal Cancer Patient Survival Using Clinical Data and Gene Expression Profiles

Comprehensive analysis of the correlation between GSTM1 and tumor immunity in colon cancer

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

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