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Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer
BACKGROUND: The poor clinical accuracy to predict the survival of colon cancer patients is associated with a high incidence rate and a poor 3-year survival rate. This study aimed to identify the poor prognostic biomarkers of colon cancer from natural killer cell-mediated cytotoxic pathway (NKCP), an...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9895796/ https://www.ncbi.nlm.nih.gov/pubmed/36742322 http://dx.doi.org/10.3389/fimmu.2023.1117908 |
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author | Ye, Zhen Zhang, Huanhuan Liang, Jianwei Yi, Shuying Zhan, Xianquan |
author_facet | Ye, Zhen Zhang, Huanhuan Liang, Jianwei Yi, Shuying Zhan, Xianquan |
author_sort | Ye, Zhen |
collection | PubMed |
description | BACKGROUND: The poor clinical accuracy to predict the survival of colon cancer patients is associated with a high incidence rate and a poor 3-year survival rate. This study aimed to identify the poor prognostic biomarkers of colon cancer from natural killer cell-mediated cytotoxic pathway (NKCP), and establish a logistical regression scoring model to predict its prognosis. METHODS: Based on the expressions and methylations of NKCP-related genes (NRGs) and the clinical information, dimensionality reduction screening was performed to establish a logistic regression scoring model to predict survival and prognosis. Risk score, clinical stage, and ULBP2 were used to establish a logistic regression scoring model to classify the 3-year survival period and compare with each other. Comparison of survival, tumor mutation burden (TMB), estimation of immune invasion, and prediction of chemotherapeutic drug IC50 were performed between low- and high-risk score groups. RESULTS: This study found that ULBP2 was significantly overexpressed in colon cancer tissues and colon cancer cell lines. The logistic regression scoring model was established to include six statistically significant features: S = 1.70 × stage – 9.32 × cg06543087 + 6.19 × cg25848557 + 1.29 × IFNA1 + 0.048 × age + 4.37 × cg21370856 − 8.93, which was used to calculate risk score of each sample. The risk scores, clinical stage, and ULBP2 were classified into three-year survival, the 3-year prediction accuracy based on 10-fold cross-validation was 80.17%, 67.24, and 59.48%, respectively. The survival time of low-risk score group was better than that of the high-risk score group. Moreover, compared to high-risk score group, low-risk score group had lower TMB [2.20/MB (log10) vs. 2.34/MB (log10)], higher infiltration score of M0 macrophages (0.17 vs. 0.14), and lower mean IC50 value of oxaliplatin (3.65 vs 3.78) (p < 0.05). CONCLUSIONS: The significantly upregulated ULBP2 was a poor prognostic biomarker of colon cancer. The risk score based on the six-feature logistic regression model can effectively predict the 3-year survival time. High-risk score group demonstrated a poorer prognosis, higher TMB, lower M0 macrophage infiltration score, and higher IC50 value of oxaliplatin. The six-feature logistic scoring model has certain clinical significance in colon cancer. |
format | Online Article Text |
id | pubmed-9895796 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98957962023-02-04 Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer Ye, Zhen Zhang, Huanhuan Liang, Jianwei Yi, Shuying Zhan, Xianquan Front Immunol Immunology BACKGROUND: The poor clinical accuracy to predict the survival of colon cancer patients is associated with a high incidence rate and a poor 3-year survival rate. This study aimed to identify the poor prognostic biomarkers of colon cancer from natural killer cell-mediated cytotoxic pathway (NKCP), and establish a logistical regression scoring model to predict its prognosis. METHODS: Based on the expressions and methylations of NKCP-related genes (NRGs) and the clinical information, dimensionality reduction screening was performed to establish a logistic regression scoring model to predict survival and prognosis. Risk score, clinical stage, and ULBP2 were used to establish a logistic regression scoring model to classify the 3-year survival period and compare with each other. Comparison of survival, tumor mutation burden (TMB), estimation of immune invasion, and prediction of chemotherapeutic drug IC50 were performed between low- and high-risk score groups. RESULTS: This study found that ULBP2 was significantly overexpressed in colon cancer tissues and colon cancer cell lines. The logistic regression scoring model was established to include six statistically significant features: S = 1.70 × stage – 9.32 × cg06543087 + 6.19 × cg25848557 + 1.29 × IFNA1 + 0.048 × age + 4.37 × cg21370856 − 8.93, which was used to calculate risk score of each sample. The risk scores, clinical stage, and ULBP2 were classified into three-year survival, the 3-year prediction accuracy based on 10-fold cross-validation was 80.17%, 67.24, and 59.48%, respectively. The survival time of low-risk score group was better than that of the high-risk score group. Moreover, compared to high-risk score group, low-risk score group had lower TMB [2.20/MB (log10) vs. 2.34/MB (log10)], higher infiltration score of M0 macrophages (0.17 vs. 0.14), and lower mean IC50 value of oxaliplatin (3.65 vs 3.78) (p < 0.05). CONCLUSIONS: The significantly upregulated ULBP2 was a poor prognostic biomarker of colon cancer. The risk score based on the six-feature logistic regression model can effectively predict the 3-year survival time. High-risk score group demonstrated a poorer prognosis, higher TMB, lower M0 macrophage infiltration score, and higher IC50 value of oxaliplatin. The six-feature logistic scoring model has certain clinical significance in colon cancer. Frontiers Media S.A. 2023-01-20 /pmc/articles/PMC9895796/ /pubmed/36742322 http://dx.doi.org/10.3389/fimmu.2023.1117908 Text en Copyright © 2023 Ye, Zhang, Liang, Yi and Zhan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Ye, Zhen Zhang, Huanhuan Liang, Jianwei Yi, Shuying Zhan, Xianquan Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
title | Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
title_full | Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
title_fullStr | Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
title_full_unstemmed | Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
title_short | Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
title_sort | significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9895796/ https://www.ncbi.nlm.nih.gov/pubmed/36742322 http://dx.doi.org/10.3389/fimmu.2023.1117908 |
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