What question did this study set out to answer?

To evaluate whether salivary glycopatterns profiled via lectin microarray combined with machine learning can accurately distinguish lung cancer from benign pulmonary diseases and healthy controls.

September 17, 2026Journal of Proteome Research

Salivary Glycoprofiling via a Machine Learning-Augmented Lectin Microarray for Noninvasive Risk Stratification of Lung Cancer

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Authors

FZFan Zhang ZTZhen Tang HDHaoqi Du

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Overview

Diagnostic study demonstrates accurate lung cancer detection from salivary glycoprofiles in patients with pulmonary conditions, indicating potential for noninvasive screening.

Key Points

To evaluate whether salivary glycopatterns profiled via lectin microarray combined with machine learning can accurately distinguish lung cancer from benign pulmonary diseases and healthy controls.
Analyzed saliva samples from 307 participants partitioned into a development cohort (N=227) and an independent test cohort (N=80) using a lectin microarray.
Constructed five task-specific LASSO-logistic regression models and a combined Nom-LC model integrating clinical variables with lectin features selected by machine learning.
The Nom-LC model incorporated smoking history and four lectin signals (HHL, PNA, RCA120, and PWM), yielding an AUC of 0.908 in training and 0.907 in internal validation sets.
In the independent test cohort, the Nom-LC model achieved an AUC of 0.903 (95% CI, 0.831–0.976), with 86.36% sensitivity, 94.44% specificity, and 90.00% accuracy.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6aabb6d85f706d05830e586d https://doi.org/10.1021/acs.jproteome.6c00376

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September 17, 2026Journal of Proteome Research

Salivary Glycoprofiling via a Machine Learning-Augmented Lectin Microarray for Noninvasive Risk Stratification of Lung Cancer

View Full Paper

Ask AI

Bookmark

Authors

FZFan Zhang ZTZhen Tang HDHaoqi Du

Discussion

Member takes

Overview

Diagnostic study demonstrates accurate lung cancer detection from salivary glycoprofiles in patients with pulmonary conditions, indicating potential for noninvasive screening.

Key Points

To evaluate whether salivary glycopatterns profiled via lectin microarray combined with machine learning can accurately distinguish lung cancer from benign pulmonary diseases and healthy controls.
Analyzed saliva samples from 307 participants partitioned into a development cohort (N=227) and an independent test cohort (N=80) using a lectin microarray.
Constructed five task-specific LASSO-logistic regression models and a combined Nom-LC model integrating clinical variables with lectin features selected by machine learning.
The Nom-LC model incorporated smoking history and four lectin signals (HHL, PNA, RCA120, and PWM), yielding an AUC of 0.908 in training and 0.907 in internal validation sets.
In the independent test cohort, the Nom-LC model achieved an AUC of 0.903 (95% CI, 0.831–0.976), with 86.36% sensitivity, 94.44% specificity, and 90.00% accuracy.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6aabb6d85f706d05830e586d https://doi.org/10.1021/acs.jproteome.6c00376

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