Dataset Information

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

ABSTRACT:

Background

Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM).

Methods

A prospectively planned study using retrospectively obtained tissues was performed. In total, 103 CRLM samples and 80 non-cancer liver tissues cut from surgically extracted specimens were analyzed using PESI-MS. Mass spectra obtained by PESI-MS were classified into cancer or non-cancer groups by using logistic regression, a kind of machine learning. Next, to identify the exact molecules responsible for the difference between CRLM and non-cancerous tissues, we performed liquid chromatography-electrospray ionization-MS (LC-ESI-MS), which visualizes sample molecular composition in more detail.

Results

This diagnostic system distinguished CRLM from non-cancer liver parenchyma with an accuracy rate of 99.5%. The area under the receiver operating characteristic curve reached 0.9999. LC-ESI-MS analysis showed higher ion intensities of phosphatidylcholine and phosphatidylethanolamine in CRLM than in non-cancer liver parenchyma (P?ConclusionThe combination of PESI-MS and machine learning distinguished CRLM from non-cancer tissue with high accuracy. Phospholipids categorized as monounsaturated fatty acids contributed to the difference between CRLM and normal parenchyma and might also be a useful diagnostic biomarker and therapeutic target for CRLM.

SUBMITTER: Kiritani S

PROVIDER: S-EPMC7945316 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

Kiritani Sho S Yoshimura Kentaro K Arita Junichi J Kokudo Takashi T Hakoda Hiroyuki H Tanimoto Meguri M Ishizawa Takeaki T Akamatsu Nobuhisa N Kaneko Junichi J Takeda Sen S Hasegawa Kiyoshi K

BMC cancer 20210310 1

<h4>Background</h4>Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM).<h4>Methods</h4>A prospectively planned study using retrospectively obtaine ...[more]

PMID: 33691644

Similar Datasets

Project description:Abstract Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug?targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug?target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2?3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti?metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.

Dataset Information

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

Background

Methods

Results

Publications

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets