Diagnostic significance of plasma lipid markers and machine learning‑based algorithm for gastric cancer

Saito, Ryo; Yoshimura, Kenichi; Shoda, Katsutoshi; Furuya, Shinichi; Akaike, Hidenori; Kawaguchi, Yoshihiko; Murata, Toshifumi; Ogata, Koretsugu; Iwano, Tomohiko; Takeda, Sén; Ichikawa, Daisuke

doi:10.3892/ol.2021.12666

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…The accuracy is 0.78, which is greater than LR. Although KNN is widely used for binary classification models for the prediction of cancer 65 , with our dataset we could not achieve accuracy greater than or equal to 80%. It may be due to a small data set.…”

Section: Discussionmentioning

confidence: 68%

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Botlagunta

Bala

et al. 2023

Sci Rep

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Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.

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Section: Discussionmentioning

confidence: 68%

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Botlagunta

Bala

et al. 2023

Sci Rep

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“…Two studies have focused specifically on phospholipids associated with GC. One study only included 36 samples (20 GCs and 16 controls) 33 . The other study enrolled 199 subjects with several different gastric lesions but the scope was limited, with only 54 phospholipids tested 34 .…”

Section: Discussionmentioning

confidence: 99%

Plasma lipids signify the progression of precancerous gastric lesions to gastric cancer: a prospective targeted lipidomics study

Liu

et al. 2022

Theranostics

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Rationale: Gastric cancer (GC) is preceded by a stepwise progression of precancerous gastric lesions. Distinguishing individuals with precancerous gastric lesions that have progression potential to GC is an important need. Perturbated lipid metabolism, particularly the dysregulation of de novo lipogenesis, is involved in gastric carcinogenesis. We conducted the first prospective lipidomics study exploring lipidomic signatures for the risk of gastric lesion progression and early GC. Methods: Our two-stage study of targeted lipidomics enrolled 400 subjects from the National Upper Gastrointestinal Cancer Early Detection Program in China, including 200 subjects of GC and different gastric lesions in the discovery and validation stages. Of validation stage, 152 cases with gastric lesions were prospectively followed for the progression of gastric lesions for a median follow-up of 580 days (interquartile range 390-806 days). We examined the lipidomic signatures associated with the risk of advanced gastric lesions and their progression to GC. Our published tissue proteomic data were referred to further investigate highlighted lipids with their biologically related protein expression in gastric mucosa. Results: We identified 11 plasma lipids significantly inversely associated with the risk of gastric lesion progression and GC occurrence. These lipids were integrated as latent profiles to identify 5 clusters of lipid expression that had distinct risk of gastric lesion progression. The latent profiles significantly improved the ability to predict the progression potential of gastric lesions (AUC: 0.82 vs 0.68, Delong's P = 4.6×10 -4 ) and risk of early GC (AUC: 0.81 vs 0.55, P = 6.3×10 -5 ). Significant associations were found between highlighted lipids, their biologically correlated proteins and the risk of GC, supporting the role of the pathways involving monocarboxylic acid metabolism and lipid transport and catabolic process in GC. Conclusions: Our study revealed the lipidomic signatures associated with the risk of gastric lesion progression and GC occurrence, exhibiting translational implications for GC prevention.

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“…Only a few studies have investigated plasma lipidic alterations in GC so far. In the first one, Saito et al reported the larger upregulation for PE 36:2 in the GC group, whereas choline-containing phospholipids (PCs, SMs and various LPCs including LPC 22:6, 20:5, 20:0, 18:2 and 22:0) exhibited the most marked down-regulation in the same group [ 58 ]. By investigating the progression of pre-cancerous gastric lesions to GC in a prospective targeted lipidomic study, Liu et al identified 11 plasma lipids decreased during gastric lesion progression to GC, including three PCs, two LPCs, but also lysoPIs (LPI 18:0, LPI 20:4), free FAs (AA, alpha-linolenic acid, stearic acid), and PA 32:1 [ 59 ].…”

Section: The Lipidome Of Biofluids As a Source Of Potential Cancer Bi...mentioning

confidence: 99%

Lipid Biomarkers in Liquid Biopsies: Novel Opportunities for Cancer Diagnosis

et al. 2023

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Altered cellular metabolism is a well-established hallmark of cancer. Although most studies have focused on the metabolism of glucose and glutamine, the upregulation of lipid metabolism is also frequent in cells undergoing oncogenic transformation. In fact, cancer cells need to meet the enhanced demand of plasma membrane synthesis and energy production to support their proliferation. Moreover, lipids are precursors of signaling molecules, termed lipid mediators, which play a role in shaping the tumor microenvironment. Recent methodological advances in lipid analysis have prompted studies aimed at investigating the whole lipid content of a sample (lipidome) to unravel the complexity of lipid changes in cancer patient biofluids. This review focuses on the application of mass spectrometry-based lipidomics for the discovery of cancer biomarkers. Here, we have summarized the main lipid alteration in cancer patients’ biofluids and uncovered their potential use for the early detection of the disease and treatment selection. We also discuss the advantages of using biofluid-derived extracellular vesicles as a platform for lipid biomarker discovery. These vesicles have a molecular signature that is a fingerprint of their originating cells. Hence, the analysis of their molecular cargo has emerged as a promising strategy for the identification of sensitive and specific biomarkers compared to the analysis of the unprocessed biofluid.

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Diagnostic significance of plasma lipid markers and machine learning‑based algorithm for gastric cancer

Cited by 10 publications

References 35 publications

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Plasma lipids signify the progression of precancerous gastric lesions to gastric cancer: a prospective targeted lipidomics study

Lipid Biomarkers in Liquid Biopsies: Novel Opportunities for Cancer Diagnosis

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