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Background <p>Gastroesophageal reflux disease (GERD) and major depressive disorder (MDD) frequently co-occur, yet their shared molecular mechanisms remain poorly understood. This study aimed to identify a robust gene signature linking GERD and MDD and to evaluate its clinical relevance.</p> Methods <p>Transcriptomic data from five GEO datasets were analyzed, including three GERD datasets (52 GERD samples and 23 controls) and two MDD datasets (138 MDD samples and 76 controls). The GERD datasets were integrated after batch correction for differential expression analysis, whereas the MDD datasets were analyzed separately for WGCNA, model development, and external validation. Candidate genes were identified from the intersection of GERD differentially expressed genes and MDD-associated co-expression module genes. A total of 71 machine learning models were systematically evaluated in the MDD training cohort using stratified 10-fold cross-validation, with model selection based on area under the curve (AUC). The clinical relevance of the prioritized genes was further examined in an independent cohort of 70 GERD patients, in whom gene expression was measured and depressive symptom severity was stratified into three PHQ-9-defined groups: non-depressed (<i>n</i> = 39), mild-to-moderate depression (<i>n</i> = 19), and major depression (<i>n</i> = 12).</p> Results <p>A three-gene signature (SAMD14, PROC, and NRG1) was identified. Among 71 candidate models, ridge regression achieved the best performance, with a cross-validated AUC of 0.709. After refitting, the model yielded an AUC of 0.896 (95% CI, 0.851–0.941) in the training cohort and 0.875 (95% CI, 0.712–1.000) in the external test cohort. Additional performance metrics included a sensitivity of 0.805, specificity of 0.844, accuracy of 0.818, and F1 score of 0.855 in the training set. In the clinical cohort, expression levels of SAMD14, PROC, and NRG1 differed significantly across depression severity groups (all <i>P</i> &lt; 0.001). DeMeester scores also differed among groups (<i>P</i> &lt; 0.001), with higher values observed in patients with more severe depressive symptoms. Correlation analysis further demonstrated significant associations between gene expression and reflux-related parameters.</p> Conclusions <p>We identified a three-gene signature derived from molecular features shared between GERD and MDD. The signature showed discriminative performance for MDD classification and was associated with reflux-related clinical parameters in GERD patients. These findings support a hypothesis-generating molecular framework for investigating GERD-MDD overlap, while further prospective and mechanistic validation remains required.</p>

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Shared molecular signatures linking gastroesophageal reflux disease and major depressive disorder revealed by integrated machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Zirong Wang,
  • Yongjia Chai,
  • Sida Pi,
  • Dilihumaer Tuerxun,
  • Yixuan Zhu,
  • Zihan Liu,
  • Huayang Zhang,
  • Jingyu Li,
  • Chen Wang,
  • Zihao Li,
  • Ying Kang,
  • Yanan Li,
  • Na Zhao,
  • Yan Tian,
  • Kai Xiong,
  • Peng Zhang

摘要

Background

Gastroesophageal reflux disease (GERD) and major depressive disorder (MDD) frequently co-occur, yet their shared molecular mechanisms remain poorly understood. This study aimed to identify a robust gene signature linking GERD and MDD and to evaluate its clinical relevance.

Methods

Transcriptomic data from five GEO datasets were analyzed, including three GERD datasets (52 GERD samples and 23 controls) and two MDD datasets (138 MDD samples and 76 controls). The GERD datasets were integrated after batch correction for differential expression analysis, whereas the MDD datasets were analyzed separately for WGCNA, model development, and external validation. Candidate genes were identified from the intersection of GERD differentially expressed genes and MDD-associated co-expression module genes. A total of 71 machine learning models were systematically evaluated in the MDD training cohort using stratified 10-fold cross-validation, with model selection based on area under the curve (AUC). The clinical relevance of the prioritized genes was further examined in an independent cohort of 70 GERD patients, in whom gene expression was measured and depressive symptom severity was stratified into three PHQ-9-defined groups: non-depressed (n = 39), mild-to-moderate depression (n = 19), and major depression (n = 12).

Results

A three-gene signature (SAMD14, PROC, and NRG1) was identified. Among 71 candidate models, ridge regression achieved the best performance, with a cross-validated AUC of 0.709. After refitting, the model yielded an AUC of 0.896 (95% CI, 0.851–0.941) in the training cohort and 0.875 (95% CI, 0.712–1.000) in the external test cohort. Additional performance metrics included a sensitivity of 0.805, specificity of 0.844, accuracy of 0.818, and F1 score of 0.855 in the training set. In the clinical cohort, expression levels of SAMD14, PROC, and NRG1 differed significantly across depression severity groups (all P < 0.001). DeMeester scores also differed among groups (P < 0.001), with higher values observed in patients with more severe depressive symptoms. Correlation analysis further demonstrated significant associations between gene expression and reflux-related parameters.

Conclusions

We identified a three-gene signature derived from molecular features shared between GERD and MDD. The signature showed discriminative performance for MDD classification and was associated with reflux-related clinical parameters in GERD patients. These findings support a hypothesis-generating molecular framework for investigating GERD-MDD overlap, while further prospective and mechanistic validation remains required.