Identification of Comprehensive Biomarkers in Patients With Mismatch Repair-Deficient Colon Adenocarcinoma Based on Parallel Multiomics
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved..
Immunocheckpoint inhibitors have shown impressive efficacy in patients with colon cancer and other types of solid tumor that are mismatch repair-deficient (dMMR). Currently, PCR-capillary electrophoresis is one of the mainstream detection methods for dMMR, but its accuracy is still limited by germline mismatch repair (MMR) mutations, the functional redundancy of the MMR system, and abnormal methylation of MutL Homolog 1 promoter. Therefore, this study aimed to develop new biomarkers for dMMR based on artificial intelligence (AI) and pathologic images, which may help to improve the detection accuracy. To screen for the differential expression genes (DEGs) in dMMR patients and validate their diagnostic and prognostic efficiency, we used the expression profile data from the Cancer Genome Atlas (TCGA). The results showed that the expression of Immunoglobulin Lambda Joining 3 in dMMR patients was significantly downregulated and negatively correlated with the prognosis. Meanwhile, our diagnostic models based on pathologic image features showed good performance with area under the curves (AUCs) of 0.73, 0.86, and 0.81 in the training, test, and external validation sets (Jiangsu Traditional Chinese Medicine Hospital cohort). Based on gene expression and pathologic characteristics, we developed an effective prognosis model for dMMR patients through multiple Cox regression analysis (with AUC values of 0.88, 0.89, and 0.88 at 1-, 3-, and 5-year intervals, respectively). In conclusion, our results showed that Immunoglobulin Lambda Joining 3 and nucleus shape-related parameters (such as nuclear texture, nuclear eccentricity, nuclear size, and nuclear pixel intensity) were independent diagnostic and prognostic factors, suggesting that they could be used as new biomarkers for dMMR patients.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:104 |
|---|---|
| Enthalten in: |
Laboratory investigation; a journal of technical methods and pathology - 104(2024), 2 vom: 08. Feb., Seite 100306 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Zhengjun [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Biomarker |
|---|
| Anmerkungen: |
Date Completed 26.02.2024 Date Revised 29.02.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.labinv.2023.100306 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM365955582 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM365955582 | ||
| 003 | DE-627 | ||
| 005 | 20240229234613.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231227s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.labinv.2023.100306 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1311.xml |
| 035 | |a (DE-627)NLM365955582 | ||
| 035 | |a (NLM)38104864 | ||
| 035 | |a (PII)S0023-6837(23)00249-0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Zhengjun |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of Comprehensive Biomarkers in Patients With Mismatch Repair-Deficient Colon Adenocarcinoma Based on Parallel Multiomics |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 26.02.2024 | ||
| 500 | |a Date Revised 29.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a Immunocheckpoint inhibitors have shown impressive efficacy in patients with colon cancer and other types of solid tumor that are mismatch repair-deficient (dMMR). Currently, PCR-capillary electrophoresis is one of the mainstream detection methods for dMMR, but its accuracy is still limited by germline mismatch repair (MMR) mutations, the functional redundancy of the MMR system, and abnormal methylation of MutL Homolog 1 promoter. Therefore, this study aimed to develop new biomarkers for dMMR based on artificial intelligence (AI) and pathologic images, which may help to improve the detection accuracy. To screen for the differential expression genes (DEGs) in dMMR patients and validate their diagnostic and prognostic efficiency, we used the expression profile data from the Cancer Genome Atlas (TCGA). The results showed that the expression of Immunoglobulin Lambda Joining 3 in dMMR patients was significantly downregulated and negatively correlated with the prognosis. Meanwhile, our diagnostic models based on pathologic image features showed good performance with area under the curves (AUCs) of 0.73, 0.86, and 0.81 in the training, test, and external validation sets (Jiangsu Traditional Chinese Medicine Hospital cohort). Based on gene expression and pathologic characteristics, we developed an effective prognosis model for dMMR patients through multiple Cox regression analysis (with AUC values of 0.88, 0.89, and 0.88 at 1-, 3-, and 5-year intervals, respectively). In conclusion, our results showed that Immunoglobulin Lambda Joining 3 and nucleus shape-related parameters (such as nuclear texture, nuclear eccentricity, nuclear size, and nuclear pixel intensity) were independent diagnostic and prognostic factors, suggesting that they could be used as new biomarkers for dMMR patients | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a biomarker | |
| 650 | 4 | |a colon cancer | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a mismatch repair | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 650 | 7 | |a Immunoglobulins |2 NLM | |
| 700 | 1 | |a Teng, Linxin |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Zhiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Junlin |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Xiaobin |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Yunzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Haihua |e verfasserin |4 aut | |
| 700 | 1 | |a Bian, Yaoyao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Weiping |e verfasserin |4 aut | |
| 700 | 1 | |a Bi, Lei |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Laboratory investigation; a journal of technical methods and pathology |d 1952 |g 104(2024), 2 vom: 08. Feb., Seite 100306 |w (DE-627)NLM000052604 |x 1530-0307 |7 nnns |
| 773 | 1 | 8 | |g volume:104 |g year:2024 |g number:2 |g day:08 |g month:02 |g pages:100306 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.labinv.2023.100306 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 104 |j 2024 |e 2 |b 08 |c 02 |h 100306 | ||